{"has_next":true,"jobs":[{"id":"f47b2b52-9138-4056-a197-783873a96c39","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Member of Technical Staff - Voice Model","slug":"member-of-technical-staff-voice-model-5b5f6cb9","description":"ABOUT xAI \n xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. \n ABOUT THE ROLE:\n You will join the Grok Voice Model team to help build the world’s best voice AI. We deliver smooth, natural, low-latency spoken interactions — expressive, multilingual, and reliable across devices and real-time scenarios. We own the full training pipeline: massive data curation, premium audio processing, frontier speech-language pre-training, and intensive post-training to push quality, speed, and stability to the limit.\n Our goal: make talking to AI feel like conversing with the most charming, kind, and knowledgeable person imaginable. We’re seeking exceptionally smart, execution-oriented engineers to help us get there.\n RESPONSIBILITIES:\n \n Design and execute large-scale speech data curation and processing pipelines, including collection of diverse real-world audio, synthetic data generation, and automated annotation workflows to enable high-quality model training and evaluation.\n Work on pre-training and post-training of speech-language models, with targeted enhancements through supervised fine-tuning, reinforcement learning, and other techniques to ensure Grok Voice responses are accurate, factually grounded, natural and idiomatic in spoken style, conversational in tone, and fluent across multiple languages.\n Build and iterate a comprehensive evaluation framework covering objective metrics (accuracy, quality, latency, expressiveness), human preference studies, content factuality assessments, real-time interaction quality, and experimentation infrastructure to measure and improve performance.\n Work closely with product teams to integrate voice models into applications and real-time environments, define spoken interaction specifications, and handle the full lifecycle from prototype to global-scale deployment for stable, low-latency, delightful voice experiences.\n \n BASIC QUALIFICATIONS:\n \n Python expert with deep proficiency in writing clean, efficient code for AI/ML systems.\n Hands-on experience processing large-scale datasets using tools like Spark and Ray for cleaning, augmentation, and feature extraction.\n Proficiency in pre-training and post-training speech-language models using JAX/PyTorch, including supervised fine-tuning, reinforcement learning, and optimizations for accuracy, factuality, natural spoken style, detail, and multilingual fluency.\n Ability to set up and run rigorous evaluation pipelines: objective metrics, human preference studies, content factuality checks, and iterative A/B testing to drive model improvements.\n Experience building or working with large-scale distributed training and inference systems on Kubernetes.\n Proactive, self-driven attitude — ready to grind in a fast-paced, high-caliber team to deliver outstanding voice AI experiences.\n \n COMPENSATION AND BENEFITS:\n $150,000 - $450,000 USD\n Base salary is just one part of our total rewards package at xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks.\n xAI is an equal opportunity employer. For details on data processing, view our  Recruitment Privacy Notice .","salary_min":150000,"salary_max":450000,"location":"Palo Alto, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["speech","reinforcement-learning","pre-training","pytorch","fine-tuning","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/5051966007","is_featured":true,"is_sticky":false,"status":"active","published_at":"2026-03-16T20:39:18Z","expires_at":"2026-06-29T14:02:58.935925Z","created_at":"2026-04-13T09:38:43.3144Z","updated_at":"2026-05-30T14:02:59.041832Z","company_name":"xAI","company_slug":"xai","company_logo_url":"https://www.google.com/s2/favicons?domain=x.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f47b2b52-9138-4056-a197-783873a96c39"},{"id":"a3d16455-f42f-4915-8723-2d023a5b665b","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Senior Software Engineer II, AI Labs \u0026 Foundations","slug":"senior-software-engineer-ii-ai-labs-foundations-e74eb4cd","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview\n Join Instacart's mission to transform grocery shopping through frontier AI. As a Senior Software Engineer on AI Labs \u0026 Foundations, you will design, build, and operate the high-scale production systems that power our most ambitious AI experiences—from Cart Assistant, our conversational shopping agent, to voice AI interactions and beyond. This is a high-impact opportunity to work at the intersection of robust software engineering and cutting-edge production AI/ML, directly shaping products used by millions of customers every day.\n We are hiring a Senior Software Engineer who will participate in the design and delivery of production AI systems, identify high-leverage technical opportunities, and contribute hands-on to AI-native products across Instacart's platform. We value bottom-up ideas, high engineering quality, and close partnership with Product, Data Science, ML, and Infrastructure teams. If you enjoy inventing, navigating ambiguity, prototyping fast, and turning wild ideas into real, scalable products, this is the team for you.\n AI Labs \u0026 Foundations sits at the intersection of frontier AI research and production engineering. Our portfolio spans the full stack of AI innovation at Instacart, including building and launching Cart Assistant, pioneering voice AI interactions, and constructing the foundational systems that power these cutting-edge experiences. We are a fast-moving, collaborative team that thrives on 0-to-1 thinking, shares learnings openly, and ships with urgency by prototyping fast and testing rigorously.\n About the Job\n \n Design, build, and operate production AI-powered systems and agentic experiences (including Cart Assistant and voice AI) that directly impact how millions of customers shop.\n Build foundational systems for cutting-edge AI experiences, ranging from embedding infrastructure and voice AI pipelines, to client facing components and integrations, by prototyping bold ideas and productizing what works.\n Integrate foundation models via APIs and open-source frameworks; apply techniques like retrieval-augmented generation and vector search where appropriate.\n Own projects end-to-end: requirements, technical design, implementation, testing, deployment, observability, and iterative improvement focused on reliability, latency, and cost efficiency.\n Collaborate with cross-functional partners in product, design, data science, and infrastructure to ship AI features end-to-end.\n Drive engineering excellence, including thoughtful system design, rigorous code review, and technical leadership that includes defining and promoting best practices for AI/ML production engineering across the team.\n \n About You\n Minimum Qualifications: \n \n Proven senior software engineer who has built, shipped, and operated production systems at scale. You make architectural calls, own what you build, and deliver through ambiguity.\n Hands-on experience with AI or ML in production. You've shipped LLM-powered features or integrated foundation model APIs into a live product, demonstrating the necessary expertise at the intersection of robust software engineering and deep production ML.\n Experience owning services end-to-end, including CI/CD, automated testing, observability (logging, metrics, tracing), and on-call participation.\n Strong communicator who partners well across disciplines - you want to get to the right answer, not just defend the first one.\n Excitement and ability to leverage cutting-edge development tools, including AI assistance (e.g., Copilot, Cursor, Claude), to maximize velocity.\n \n Preferred Qualifications: \n \n 5 to 8+ years of industry experience.\n A track record of 0-to-1 work taking unconventional ideas from prototype through rapid iteration to production.\n Experience building conversational agents, multi-turn dialogue systems, or agentic LLM applications.\n Exp","salary_min":192000,"salary_max":202000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["cloud","fine-tuning","code-generation","generative-ai","llm","distributed-systems","agents","speech"],"apply_url":"https://instacart.careers/job/?gh_jid=7951041","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T22:43:14Z","expires_at":"2026-06-29T14:08:42.057285Z","created_at":"2026-05-30T14:08:42.180879Z","updated_at":"2026-05-30T14:08:42.180879Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a3d16455-f42f-4915-8723-2d023a5b665b"},{"id":"57221d71-7c6d-475a-8bf7-a9a2f0b6c944","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Staff Platform Engineer, Voice AI","slug":"staff-platform-engineer-voice-ai-d5bc66dc","description":"About the Role \n Together AI is defining the infrastructure layer for the next generation of voice applications. Our Voice AI platform powers production-grade, real-time voice agents at scale — and we're looking for a Staff Platform Engineer to own the architecture that makes it possible. \n This isn't a role about maintaining what exists. You'll set the technical direction for how developers interact with Together's voice platform — from the real-time API primitives they build on, to the autoscaling systems that keep latency SLOs intact under unpredictable load, to the multi-provider abstraction layer that makes our platform uniquely powerful. Voice infrastructure is categorically harder than text inference: bidirectional audio streams, stateful long-lived connections, millisecond latency requirements, and complex multi-model routing don't forgive architectural shortcuts. You'll bring the judgment to get this right the first time, at scale. \n This is a foundational hire on a small, high-conviction team. The decisions you make in this role will define the platform architecture for years. \n Responsibilities \n \n Own the architecture and reliability of Together's real-time API layer — set the technical direction for WebSocket and HTTP streaming APIs powering STT and TTS at scale; establish the reliability bar (connection lifecycle, backpressure, graceful degradation, reconnection) that production voice agents — contact centers, AI agents, communication platforms — depend on. \n Lead autoscaling architecture for latency-sensitive voice workloads — design and ship orchestration systems that handle bursty, real-time traffic across tens of thousands of GPUs; solve the hard problems at the intersection of concurrent connection limits, streaming state, and hard latency ceilings that generic autoscalers weren't built for. \n Define the voice API feature surface — make the architectural calls on word-level alignment, real-time speaker diarization, audio format support (g711/mulaw, PCM, WebRTC), pronunciation controls, and multi-context WebSocket — with a clear view of what unlocks the next category of developer use cases. \n Build the observability platform for voice infrastructure — design the latency breakdown pipelines, audio quality signal collection, and customer-facing dashboards that give both the team and developers the instrumentation they need to operate at production quality; make debugging voice issues fast and systematic. \n Own the multi-provider abstraction layer — architect the normalization layer across model partners (Cartesia, Deepgram, Rime, and others) that delivers consistent, provider-agnostic API behavior; your design should absorb upstream variability without exposing it to developers. \n Drive the interface between API and ML serving — partner closely with ML engineering leadership to define the contract between the API layer and the model serving stack; your decisions here have direct impact on end-to-end latency and reliability SLAs. \n Raise the bar for developer experience across the platform — lead API design reviews, shape documentation strategy, define integration patterns and cookbooks; the voice developer experience should be something the industry references, not just adequate. \n Architect for the product surface that doesn't exist yet — build systems with the foresight that they become the foundation for multiple new voice products; your platform decisions should expand what's possible, not constrain it. \n \n Requirements \n \n 8+ years of experience building large-scale, real-time distributed systems — with clear ownership of systems that carried production traffic at meaningful scale; you can speak to the architectural decisions you made and defend the tradeoffs. \n Deep, battle-tested expertise in real-time streaming infrastructure — WebSocket server architecture, SSE, bidirectional streaming, connection multiplexing, stateful protocol design — you've debugged production failures in these systems and come out with durable architectural improvements. \n Expert-level TypeScript and Python, with strong proficiency in systems-level thinking; Rust experience is a meaningful advantage at this level given where voice infrastructure is heading. \n Senior distributed systems judgment — load balancing, autoscaling, rate limiting, and traffic shaping for latency-sensitive workloads aren't concepts you reference, they're problems you've solved under pressure. \n Deep Kubernetes expertise — custom autoscalers, resource management, and health checking for stateful, streaming services; you've built Kubernetes automation that handled edge cases the off-the-shelf tooling couldn't. \n Strong technical leadership — you set direction, influence across teams without authority, bring clarity to ambiguous problems, and leave systems and teams meaningfully better than you found them. \n Sharp product intuition for developer platforms — you have genuine opinions about API ergonom","salary_min":220000,"salary_max":280000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["api-design","speech","agents","mlops","distributed-systems","platform"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5142176007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-19T19:32:06Z","expires_at":"2026-06-29T14:01:50.490998Z","created_at":"2026-05-27T14:02:00.784235Z","updated_at":"2026-05-30T14:01:50.599493Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/57221d71-7c6d-475a-8bf7-a9a2f0b6c944"},{"id":"bc38cbd7-6147-49eb-a610-64fb031af669","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Staff Machine Learning Engineer, Voice AI ","slug":"staff-machine-learning-engineer-voice-ai-049973bf","description":"About the Role \n Together AI is building the best inference infrastructure for voice applications. Our Voice AI platform powers production-grade, real-time voice agents and applications — serving speech-to-text and text-to-speech models with best-in-class latency and reliability.\n We're looking for a Staff ML Engineer to drive the model serving layer for voice workloads. You'll work hands-on with inference engines like TRT-LLM and SGLang to optimize how we serve models like Whisper, Parakeet, Orpheus, and Kokoro — pushing latency and throughput to the frontier. You'll profile GPU utilization, design batching strategies for streaming audio, and ensure new model architectures can go from research to production quickly.\n This is a foundational hire on a small, high-impact team. Voice inference has unique challenges — streaming audio, tokenization, real-time latency budgets — that require dedicated ML engineering focus. You'll shape how Together serves voice models as the industry moves from pipeline architectures (ASR → LLM → TTS) toward end-to-end speech-to-speech.\n \n Own the model serving stack that powers Together's voice platform across STT, TTS, and speech-to-speech.\n Work directly with state-of-the-art accelerators (H100s, H200s, B200s) to optimize voice model inference.\n Collaborate with model partners (Cartesia, Deepgram, Rime, and others) to bring their models to production on Together's infrastructure.\n Build quality evaluation frameworks that guide model selection for customers and inform the roadmap.\n Join a small, early-stage team with outsized impact on a fast-growing product area.\n \n  \n Responsibilities \n \n Own the voice inference roadmap end-to-end — define and execute the technical strategy for optimizing STT, TTS, and speech-to-speech models across Together's infrastructure, with a clear-eyed view of where the field is heading and how to position the platform ahead of it.\n Drive best-in-class inference performance — architect and implement systems targeting leading TTFB, throughput, and GPU utilization for voice workloads; set the performance bar others in the industry measure against, not just catch up to.\n Lead productionization of voice models at scale — design the serving architecture for serverless and dedicated endpoints, including batching strategies, streaming inference pipelines, and memory management tailored to real-time audio; own reliability and latency SLAs.\n Build the voice evaluation platform — design a rigorous, extensible evaluation framework covering WER across accents, languages, and noise conditions for STT; naturalness, latency, and pronunciation fidelity for TTS; establish the internal benchmark methodology that informs model selection and roadmap decisions.\n Shape the architecture for next-generation model support — anticipate and enable emerging model paradigms — audio-native LLMs, codec-based architectures (SNAC, Encodec), and end-to-end speech-to-speech systems — before they're mainstream, not after.\n Serve as the technical DRI for model partner integrations — lead deep collaboration with partners such as Cartesia, Deepgram, and Rime; own the full lifecycle from integration to optimization to ongoing performance accountability.\n Diagnose and resolve the hardest performance problems in the stack — conduct systematic profiling and root-cause analysis from GPU kernel behavior to framework-level bottlenecks; drive shipped improvements with documented, measurable impact.\n Influence platform architecture across the organization — partner with platform engineering leadership to ensure the serving layer is built for the latency and reliability demands of real-time voice APIs; your technical decisions should raise the ceiling for the whole team.\n Define and scale voice fine-tuning capabilities — lead the technical direction for enabling customers to fine-tune STT and TTS models on Together's infrastructure, establishing the primitives for differentiated voice experiences.\n Lay technical foundations for a category-defining product surface — architect systems with enough foresight that they support multiple new voice products with minimal rework; think in terms of platforms, not point solutions.\n \n Requirements \n \n 8+ years of ML engineering experience, with a demonstrated focus on model serving, inference optimization, or ML infrastructure at production scale — including systems you've owned from design through live traffic.\n Deep, practical expertise in LLM serving engines (vLLM, SGLang, TensorRT-LLM, or equivalent) — you've modified engine internals, debugged edge cases under load, and contributed improvements back; you don't stop at the API surface.\n Expert-level Python and PyTorch proficiency, with a strong command of GPU optimization — CUDA kernels, memory hierarchies, profiling toolchains — and a track record of turning that knowledge into shipped latency or throughput wins.\n Proven system design judgment — you've made arch","salary_min":220000,"salary_max":280000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["mlops","gpu","llm","pytorch","fine-tuning","speech","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5140763007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-19T18:19:46Z","expires_at":"2026-06-29T14:01:50.400776Z","created_at":"2026-05-27T14:02:00.695384Z","updated_at":"2026-05-30T14:01:50.521421Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bc38cbd7-6147-49eb-a610-64fb031af669"},{"id":"b9819fc1-996f-4825-bec6-a24dd9a53bdc","company_id":"0fc88a91-688e-421d-917d-4880569dd976","title":"Research Engineer, Voice","slug":"research-engineer-voice-ae96b0ee","description":"About Inflection AI \n Inflection AI is a Public Benefit Corporation empowering people with human-centered, emotionally intelligent AI. We’re shaping the future of AI by combining emotional intelligence (EQ) and raw intelligence (IQ) to elevate people’s potential. Inflection AI created Pi, the world’s first emotionally intelligent AI, to help people work through decisions, emotions, and challenges. Pi is a personal AI agent powered by Inflection AI’s foundation model, proving that AI can be personal, empathetic, and contextually aware.\n About the Role \n We’re looking for a Member of Technical Staff (MTS), Research Engineer focused on voice and audio to help advance the spoken intelligence behind Pi. In this role, you’ll work at the intersection of research and production—developing, training, and shipping neural models across the full spectrum of voice: speech synthesis, recognition, audio generation, and real-time spoken dialogue. You’ll collaborate closely with ML engineers, product teams, and infrastructure to turn cutting-edge ideas in areas like neural audio codecs, diffusion-based TTS, and multimodal foundation models into the natural, expressive voice experiences that millions of Pi users interact with every day.\n What You’ll Do \n \n Research, develop, and optimize neural models for voice and audio—including text-to-speech, automatic speech recognition, audio generation, and spoken dialogue systems.\n Build and maintain production-grade training and inference pipelines for voice models, with close attention to latency, naturalness, and scalability.\n Run experiments end-to-end: data curation, model architecture design, training, evaluation, and ablation studies.\n Collaborate with ML engineers, product teams, and infrastructure to integrate voice models into Pi’s real-time conversational stack.\n Explore and apply advances in neural audio codecs, diffusion-based synthesis, streaming architectures, and multimodal foundation models to improve Pi’s voice experience.\n Develop robust evaluation frameworks combining perceptual metrics, automated benchmarks, and user-facing quality signals.\n Contribute to Inflection’s research culture through publications, internal reviews, and knowledge sharing.\n \n What We’re Looking For \n \n 2-5 years of research or engineering experience (including graduate work) in audio, speech, or multimodal ML.\n Strong proficiency in PyTorch and hands-on experience training and debugging large-scale neural models on GPU/accelerator clusters.\n Solid understanding of audio and speech fundamentals spectrograms, mel features, vocoders, codec-based representations, and signal processing.\n Demonstrated ability to take a research idea from prototype to production: equally comfortable reading papers and writing efficient, CUDA-aware training loops.\n Familiarity with modern generative architectures for audio (e.g., diffusion models, autoregressive codecs, flow-matching) and their trade-offs.\n Clear, collaborative communication able to distill complex research into actionable insights for cross-functional partners.\n Have a bachelor’s degree or equivalent in Computer Science, Electrical Engineering, Linguistics, or a related field; MS or PhD strongly preferred.\n \n Employee Pay Disclosures \n At Inflection AI, we aim to attract and retain the best employees and compensate them in a way that appropriately and fairly values their individual contributions to the company. For this role, Inflection AI estimates a starting annual base salary to fall within the range of $225,000 to $325,000 , depending on a candidate’s qualifications and level of experience. This role also includes a meaningful equity component, allowing employees to share in the long-term success of the company.\n Benefits \n Inflection AI values and supports our team’s mental and physical health. We are focused on building a positive, safe, inclusive and inspiring place to work. Our benefits include: \n \n Diverse medical, dental and vision options \n 401k matching program \n Unlimited paid time off \n Parental leave and flexibility for all parents and caregivers\n Support of country-specific visa needs for international employees living in the Bay Area","salary_min":225000,"salary_max":325000,"location":"Palo Alto, CA","workplace":"onsite","job_type":"full-time","experience_level":"junior","tags":["speech","pytorch","search","generative-ai","gpu","diffusion-models","agents","research"],"apply_url":"https://boards.greenhouse.io/inflectionai/jobs/4681124006?gh_jid=4681124006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-12T20:45:47Z","expires_at":"2026-06-29T14:04:38.202433Z","created_at":"2026-05-14T14:05:27.875309Z","updated_at":"2026-05-30T14:04:38.315108Z","company_name":"Inflection AI","company_slug":"inflection-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=inflection.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b9819fc1-996f-4825-bec6-a24dd9a53bdc"},{"id":"e166cabe-5ba5-4fe5-a30d-688ddd5f8fc1","company_id":"5dfcd8fc-f8dd-4f46-b613-ca6da467ff4b","title":"Machine Learning Researcher, Audio","slug":"machine-learning-researcher-audio-6b0906fa","description":"MACHINE LEARNING RESEARCHER, AUDIO\n\nLocation: San Francisco, CA or Remote (US)\n\n \n \n\n\nABOUT BLAND\n\nAt Bland.com, our mission is to empower enterprises to build AI phone agents at scale. Based in San Francisco, we are a fast-growing team reimagining how customers interact with businesses through voice. We have raised $65 million from leading Silicon Valley investors, including Emergence Capital, Scale Venture Partners, Y Combinator, and founders of Twilio, Affirm, and ElevenLabs.\n\n \n\nVoice is quickly becoming the primary interface between businesses and their customers. We are building the models and infrastructure that make those interactions feel natural, reliable, and genuinely human.\n\n \n \n\n\nTHE ROLE: MACHINE LEARNING RESEARCHER, AUDIO\n\nAs a Machine Learning Researcher at Bland, you'll be working on foundational research and development across the core components of our voice stack: speech-to-text, large language models, neural audio codecs, and text-to-speech. Your work will define how our agents understand, reason, and speak in real time at enterprise scale.\n\n \n\nThis is not a narrow research role. You will take ideas from theory to large-scale training to production inference systems serving millions of calls per day. You will design new modeling approaches, validate them with rigorous experimentation, and collaborate with engineering teams to deploy them into real customer environments.\n\n \n \n\n\nWHAT YOU WILL DO\n\nBuild and Scale Next-Generation TTS Systems\n\n - Design and train large scale text-to-speech models capable of expressive, controllable, human-sounding output.\n\n - Develop neural audio codec-based TTS architectures for efficient, high-fidelity generation.\n\n - Improve prosody modeling, question inflection, emotional expression, and multi-speaker robustness.\n\n - Optimize for real-time, low-latency inference in production.\n\n \n\nAdvance Speech-to-Text Modeling\n\n - Build and fine-tune large scale ASR systems robust to accents, noise, telephony artifacts, and code switching.\n\n - Leverage self-supervised pretraining and large-scale weak supervision.\n\n - Improve transcription accuracy for real-world enterprise scenarios, including structured extraction and conversational nuance.\n\n \n\nPioneer Neural Audio Codecs\n\n - Research and implement neural audio codecs that achieve extreme compression with minimal perceptual loss.\n\n - Explore discrete and continuous latent representations for scalable speech modeling.\n\n - Design codec architectures that enable downstream generative modeling and controllable synthesis.\n\n \n\nDevelop Scalable Training Pipelines\n\n - Curate and process massive audio datasets across languages, speakers, and environments.\n\n - Design staged training curricula and data filtering strategies.\n\n - Scale training across distributed GPU clusters focusing on cost, throughput, and reliability.\n\n \n\nRun Rigorous Experiments\n\n - Design ablation studies that isolate the impact of architectural changes.\n\n - Measure improvements using both objective metrics and perceptual evaluations.\n\n - Validate ideas quickly through focused experiments that confirm or eliminate hypotheses.\n\n \n \n\n\nWHAT MAKES YOU A GREAT FIT\n\nDeep Research Foundations\n\n - Experience with self-supervised learning, multimodal modeling, or generative modeling.\n\n - Ability to derive new formulations and implement them efficiently.\n\n \n\nExpertise in Voice Modeling\n\n - Hands-on experience building or scaling TTS, STT, or neural audio codec systems.\n\n - Familiarity with large scale speech datasets and real-world audio variability.\n\n - Strong intuition for audio quality, prosody, and conversational dynamics.\n\n \n\nSystems and Hardware Awareness\n\n - Experience training and serving large models on modern accelerators.\n\n - Knowledge of inference optimization techniques, including quantization, kernel optimization, and memory efficiency.\n\n - Understanding of real-time constraints in telephony or streaming environments.\n\n \n\nExperimental Rigor\n\n - Track record of designing controlled experiments and meaningful ablations.\n\n - Comfortable working with both offline benchmarks and live production metrics.\n\n - Ability to move quickly from hypothesis to validation.\n\n \n\nBuilder Mentality\n\n - Comfortable in fast-moving startup environments.\n\n - Strong ownership mindset from research through deployment.\n\n - Excited by ambiguous, unsolved problems.\n\n \n \n\n\nHOW YOU SHOW UP\n\n - You treat unsolved problems as opportunities to invent new paradigms.\n\n - You identify the single experiment that can validate an idea in days, not months.\n\n - You measure everything and let data drive decisions.\n\n - You are obsessed with making voice agents sound truly human.\n\n - You use AI tools aggressively to amplify your own impact and accelerate research cycles.\n\n \n \n\n\nBONUS POINTS\n\n - Experience with large scale distributed training.\n\n - Research publications or open source contributions in speech or language AI.\n\n - Background in real-time speech systems or telephon","salary_min":160000,"salary_max":250000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["gpu","distributed-systems","healthcare","speech","llm","pre-training","machine-learning","research"],"apply_url":"https://jobs.ashbyhq.com/bland/2e815d0d-8e7a-43cc-8853-c1b029aeb499/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-20T22:07:11.702Z","expires_at":"2026-06-29T14:06:16.806176Z","created_at":"2026-04-22T15:40:14.708917Z","updated_at":"2026-05-30T14:06:16.920768Z","company_name":"Bland AI","company_slug":"bland-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=bland.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e166cabe-5ba5-4fe5-a30d-688ddd5f8fc1"},{"id":"92e0e6b0-3459-44ec-9e1a-4e36a7b805d4","company_id":"4d985fa4-b897-4f93-9745-c332367ad86b","title":"Research Scientist - LLM ","slug":"research-scientist-llm-80f40837","description":"ABOUT RETELL AI\n\nRetell AI is using first principles to reimagine the call center with cutting-edge voice AI.\n\nThousands of companies now utilize Retell’s AI voice agents to handle sales, support, and logistics calls that once required large teams of human agents. Backed by Y Combinator, Alt Capital, and other leading investors, we have scaled to $60M ARR with a team of 40 people, up from $5M at the start of 2025.\n\nOur vision for 2026 is to build a modern CX platform where entire contact centers are powered by AI. Instead of basic automation that needs constant human tuning, we’re creating intelligent AI “workers” that can act as frontline agents, QA analysts, and managers — continuously executing, monitoring, and improving customer interactions.\n\nWe’re growing quickly and looking for ambitious builders who want to tackle hard technical problems, move fast, and have real impact at one of the fastest-growing voice AI startups.\n\nLet’s build the future together.\n\n - We’re a top 50 AI app in a16z list: https://tinyurl.com/5853dt2x\n\n - #4 on Brex's Fast-Growing Software Vendors of 2025: https://www.brex.com/journal/brex-benchmark-december-2025\n\n - We're also one of the top ranking startups on: https://leanaileaderboard.com/\n\n - Enterprise tech 30: https://www.wing.vc/et30/overview\n\n\n\n\nABOUT THE ROLE\n\nThis is a research-driven, high-impact role for ML researchers who want to push the boundaries of real-time AI. As a Founding Machine Learning Research Engineer at Retell, you’ll focus on advancing model capabilities for human-like voice agents operating in complex, real-world environments.\n\nYou’ll explore new approaches across LLMs and audio models, design novel evaluation methods, and prototype systems that improve reasoning, latency, and conversational quality. Your work will directly influence production systems, bridging cutting-edge research with real-world deployment.\n\nIf you’re excited about solving open-ended ML problems, experimenting rapidly, and shaping how voice AI systems think and perform, this is a unique opportunity to do so at scale.\n\n\n\n\nKEY RESPONSIBILITIES\n\n - Research \u0026 Experimentation – Explore and develop new techniques across LLMs and audio models to improve reasoning, latency, and conversational quality in real-time systems.\n\n - Model Training – Rapidly build and iterate on models and pipelines, turning research ideas into working prototypes. Innovate on paradigms, training methods, and inference.\n\n - Evaluation \u0026 Benchmarking – Design novel evaluation frameworks, datasets, and metrics to measure performance on complex, real-world voice tasks.\n\n - Bridge Research to Production – Collaborate closely with engineering to translate research insights into deployable systems.\n\n - Human Feedback Loops – Develop methods to incorporate human evaluation into model improvement, especially for subjective conversational quality.\n\n - Advance the Frontier – Stay at the cutting edge of ML research and bring new ideas into Retell’s product and infrastructure.\n\n\n\n\nREQUIRED\n\n - Strong ML Research Background – You've worked on advanced ML problems (like LLM pre-training and post-training, transcription model training, TTS, or multimodal systems), either in industry or academia.\n\n - Deep Technical Foundation – Comfortable with PyTorch, model architectures, and the math behind modern machine learning.\n\n - Top Academic Background – Master's degree in CS, ML, AI or related field required; PhD preferred. Equivalent research-level engineering experience also considered.\n\n\n\n\nYOU MIGHT THRIVE IF YOU\n\n - Published or Awarded – First/co-author publications at top-tier venues (NeurIPS, ICML, ICLR, ACL, Interspeech, etc.) or notable competition awards are a strong plus.\n\n - Experimental Mindset – You enjoy exploring open-ended problems and iterating quickly on ideas.\n\n - Bridge Theory \u0026 Practice – You can translate research into systems that work in real-world environments.\n\n - Startup-Ready – You thrive in fast-paced environments with high ownership and ambiguity.\n\n - Collaborative \u0026 Clear Communicator – You can explain complex ideas and work cross-functionally to drive impact.\n\n\n\n\nJOB DETAILS\n\n - Cash: $225,000 - $400,000 base salary\n\n - Equity: Offers Equity\n\n - Location: Redwood City, CA, US (100% Relocation Provided)\n\n - US Visas: Retell AI is open to sponsoring work authorization for qualified candidates, including H1B/H-1B, TN, L-1, E-3, F-1 (OPT/CPT), and O-1 visas.\n\n\n\n\nOTHER BENEFITS\n\n - 100% coverage for medical, dental, and vision insurance\n\n - $70/day DoorDash credit for unlimited meals and snacks\n\n - $200/month wellness reimbursement\n\n - $300/month commuter reimbursement\n\n - $75/month phone bill reimbursement\n\n - $50/month internet reimbursement\n   \n   \n\n\nCOMPENSATION PHILOSOPHY\n\n - Best Offer Upfront: Choose from three cash-equity balance options, no negotiation needed\n\n - Top 1% Talent: Above-market pay (top 5 percentile)\n\n - High Ownership: Small teams, \u003e$","salary_min":225000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["llm","pytorch","search","speech","pre-training","research"],"apply_url":"https://jobs.ashbyhq.com/retell-ai/b0d780eb-df25-49d0-859a-915de204a2f2/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-14T05:52:56.477Z","expires_at":"2026-06-29T14:11:20.587907Z","created_at":"2026-04-16T11:17:45.913083Z","updated_at":"2026-05-30T14:11:20.696475Z","company_name":"Retell AI","company_slug":"retell-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=retellai.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/92e0e6b0-3459-44ec-9e1a-4e36a7b805d4"},{"id":"acc5d396-6aa2-40ba-8a49-632774606bde","company_id":"4d985fa4-b897-4f93-9745-c332367ad86b","title":"Research Scientist - Audio ","slug":"research-scientist-audio-918408c6","description":"ABOUT RETELL AI\n\nRetell AI is using first principles to reimagine the call center with cutting-edge voice AI.\n\nThousands of companies now utilize Retell’s AI voice agents to handle sales, support, and logistics calls that once required large teams of human agents. Backed by Y Combinator, Alt Capital, and other leading investors, we have scaled to $60M ARR with a team of 40 people, up from $5M at the start of 2025.\n\nOur vision for 2026 is to build a modern CX platform where entire contact centers are powered by AI. Instead of basic automation that needs constant human tuning, we’re creating intelligent AI “workers” that can act as frontline agents, QA analysts, and managers — continuously executing, monitoring, and improving customer interactions.\n\nWe’re growing quickly and looking for ambitious builders who want to tackle hard technical problems, move fast, and have real impact at one of the fastest-growing voice AI startups.\n\nLet’s build the future together.\n\n - We’re a top 50 AI app in a16z list: https://tinyurl.com/5853dt2x\n\n - #4 on Brex's Fast-Growing Software Vendors of 2025: https://www.brex.com/journal/brex-benchmark-december-2025\n\n - We're also one of the top ranking startups on: https://leanaileaderboard.com/\n\n - Enterprise tech 30: https://www.wing.vc/et30/overview\n\n\n\n\nABOUT THE ROLE\n\nThis is a research-driven, high-impact role for ML researchers who want to push the boundaries of real-time AI. As a Founding Machine Learning Research Engineer at Retell, you’ll focus on advancing model capabilities for human-like voice agents operating in complex, real-world environments.\n\nYou’ll explore new approaches across LLMs and audio models, design novel evaluation methods, and prototype systems that improve reasoning, latency, and conversational quality. Your work will directly influence production systems, bridging cutting-edge research with real-world deployment.\n\nIf you’re excited about solving open-ended ML problems, experimenting rapidly, and shaping how voice AI systems think and perform, this is a unique opportunity to do so at scale.\n\n\n\n\nKEY RESPONSIBILITIES\n\n - Research \u0026 Experimentation – Explore and develop new techniques across LLMs and audio models to improve reasoning, latency, and conversational quality in real-time systems.\n\n - Model Training – Rapidly build and iterate on models and pipelines, turning research ideas into working prototypes. Innovate on paradigms, training methods, and inference.\n\n - Evaluation \u0026 Benchmarking – Design novel evaluation frameworks, datasets, and metrics to measure performance on complex, real-world voice tasks.\n\n - Bridge Research to Production – Collaborate closely with engineering to translate research insights into deployable systems.\n\n - Human Feedback Loops – Develop methods to incorporate human evaluation into model improvement, especially for subjective conversational quality.\n\n - Advance the Frontier – Stay at the cutting edge of ML research and bring new ideas into Retell’s product and infrastructure.\n\n\n\n\nREQUIRED\n\n - Strong ML Research Background – You've worked on advanced ML problems (like LLM pre-training and post-training, transcription model training, TTS, or multimodal systems), either in industry or academia.\n\n - Deep Technical Foundation – Comfortable with PyTorch, model architectures, and the math behind modern machine learning.\n\n - Top Academic Background – Master's degree in CS, ML, AI or related field required; PhD preferred. Equivalent research-level engineering experience also considered.\n\n \n\n\nYOU MIGHT THRIVE IF YOU\n\n - Published or Awarded – First/co-author publications at top-tier venues (NeurIPS, ICML, ICLR, ACL, Interspeech, etc.) or notable competition awards are a strong plus.\n\n - Experimental Mindset – You enjoy exploring open-ended problems and iterating quickly on ideas.\n\n - Bridge Theory \u0026 Practice – You can translate research into systems that work in real-world environments.\n\n - Startup-Ready – You thrive in fast-paced environments with high ownership and ambiguity.\n\n - Collaborative \u0026 Clear Communicator – You can explain complex ideas and work cross-functionally to drive impact.\n\n\n\n\nJOB DETAILS\n\n - Cash: $225,000 - $400,000 base salary\n\n - Equity: Offers Equity\n\n - Location: Redwood City, CA, US (100% Relocation Provided)\n\n - US Visas: Retell AI is open to sponsoring work authorization for qualified candidates, including H1B/H-1B, TN, L-1, E-3, F-1 (OPT/CPT), and O-1 visas.\n\n\n\n\nOTHER BENEFITS\n\n - 100% coverage for medical, dental, and vision insurance\n\n - $70/day DoorDash credit for unlimited meals and snacks\n\n - $200/month wellness reimbursement\n\n - $300/month commuter reimbursement\n\n - $75/month phone bill reimbursement\n\n - $50/month internet reimbursement\n   \n   \n\n\nCOMPENSATION PHILOSOPHY\n\n - Best Offer Upfront: Choose from three cash-equity balance options, no negotiation needed\n\n - Top 1% Talent: Above-market pay (top 5 percentile)\n\n - High Ownership: Small teams, ","salary_min":225000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["pre-training","pytorch","speech","search","llm","research"],"apply_url":"https://jobs.ashbyhq.com/retell-ai/7dbe5404-e08c-4c62-99dc-ef050534d029/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-14T05:52:52.3Z","expires_at":"2026-06-29T14:11:20.507014Z","created_at":"2026-04-16T11:17:45.838238Z","updated_at":"2026-05-30T14:11:20.622831Z","company_name":"Retell AI","company_slug":"retell-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=retellai.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/acc5d396-6aa2-40ba-8a49-632774606bde"},{"id":"9da24f0c-dc11-4d04-8eb1-c0e2fdc44e97","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Senior Platform Engineer, Voice AI","slug":"senior-platform-engineer-voice-ai-92a1406f","description":"About the Role \n Together AI is building the best inference infrastructure for voice applications. Our Voice AI platform powers production-grade, real-time voice agents and applications — serving speech-to-text and text-to-speech models with best-in-class latency and reliability.\n We're looking for a Senior Platform Engineer to own the API and infrastructure layer for voice workloads. You'll build the real-time WebSocket and HTTP APIs that developers use to ship voice experiences, design autoscaling for latency-sensitive streaming workloads, and ensure our multi-provider voice platform is reliable enough for production voice agents handling millions of calls.\n This is a foundational hire on a small, high-impact team. Voice APIs have fundamentally different infrastructure requirements than text-based inference — bidirectional audio streaming, stateful connections, tight latency SLOs, and complex multi-model routing. You'll define how developers interact with Together's voice platform as we grow from early customers to the default infrastructure for voice AI.\n \n Own the real-time API layer (WebSocket + HTTP streaming) that powers Together's voice platform.\n Design autoscaling and orchestration for voice workloads running on tens of thousands of GPUs.\n Build the developer experience — APIs, observability, and tooling — for a fast-growing product area.\n Work with production voice customers (contact centers, AI agents, communication platforms) to ship what they actually need.\n Join a small, early-stage team with outsized impact on a new product line.\n \n Responsibilities \n \n Build and harden real-time WebSocket and HTTP streaming APIs for STT and TTS — including connection lifecycle management, backpressure, error handling, and reconnection, at the reliability bar needed for production voice agents.\n Design and ship autoscaling for voice model endpoints that handles bursty, real-time traffic patterns — accounting for concurrent connection limits, streaming state, and hard latency ceilings.\n Implement voice-specific API features: word-level alignment, speaker diarization in realtime, audio format flexibility (g711/mulaw for telephony, PCM, WebRTC formats), pronunciation controls, and multi-context WebSocket support.\n Build voice-specific observability — latency breakdowns, audio quality signals, and dashboards that help both the team and customers debug issues.\n Own multi-model normalization across our model partners (Cartesia, Deepgram, Rime, and others), ensuring consistent API behavior regardless of the underlying provider.\n Collaborate with the ML engineering side of the team on the interface between the API layer and the model serving stack, ensuring latency and reliability requirements are met end-to-end.\n Contribute to developer experience — API design, documentation, integration cookbooks, playground and showcasing how best-in-class voice agents are built.\n Lay the groundwork for multiple new products down the line.\n \n Requirements \n \n 5+ years of experience building large-scale, real-time distributed systems and API services.\n Deep expertise in real-time streaming infrastructure — WebSocket server architecture, Server-Sent Events, bidirectional streaming, connection multiplexing, and stateful protocol design.\n Expert-level programming in TypeScript and Python; experience with Rust is a plus.\n Strong distributed systems fundamentals: load balancing, autoscaling, rate limiting, and traffic shaping for latency-sensitive workloads.\n Experience with Kubernetes — including custom autoscalers, resource management, and health checking for stateful services.\n Strong product sense — you care about API ergonomics and think about what developers building voice apps actually need.\n Comfort working on a small, early-stage team where you'll wear multiple hats and move fast.\n Experience with audio or media protocols (WebRTC, g711, PCM encoding) is a strong plus.\n Familiarity with ML model serving infrastructure and how inference engines work is a plus — you'll interface with the serving layer regularly.\n Full-stack experience (React, Next.js) is a nice-to-have for contributing to developer-facing tooling.\n Bachelor's or Master's degree in Computer Science, Computer Engineering, or related field, or equivalent practical experience.\n \n About Together AI \n Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI in","salary_min":200000,"salary_max":260000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","speech","api-design","agents","mlops","platform"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5093344007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-03-30T21:40:56Z","expires_at":"2026-06-29T14:01:49.693973Z","created_at":"2026-04-13T09:37:38.328299Z","updated_at":"2026-05-30T14:01:49.801712Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9da24f0c-dc11-4d04-8eb1-c0e2fdc44e97"},{"id":"fdeb7783-851b-48d1-810b-3d39970161b6","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Senior Machine Learning Engineer, Voice AI ","slug":"senior-machine-learning-engineer-voice-ai-e60e860b","description":"About the Role \n Together AI is building the best inference infrastructure for voice applications. Our Voice AI platform powers production-grade, real-time voice agents and applications — serving speech-to-text and text-to-speech models with best-in-class latency and reliability.\n We're looking for a Senior ML Engineer to drive the model serving layer for voice workloads. You'll work hands-on with inference engines like TRT-LLM and SGLang to optimize how we serve models like Whisper, Parakeet, Orpheus, and Kokoro — pushing latency and throughput to the frontier. You'll profile GPU utilization, design batching strategies for streaming audio, and ensure new model architectures can go from research to production quickly.\n This is a foundational hire on a small, high-impact team. Voice inference has unique challenges — streaming audio, tokenization, real-time latency budgets — that require dedicated ML engineering focus. You'll shape how Together serves voice models as the industry moves from pipeline architectures (ASR → LLM → TTS) toward end-to-end speech-to-speech.\n \n Own the model serving stack that powers Together's voice platform across STT, TTS, and speech-to-speech.\n Work directly with state-of-the-art accelerators (H100s, H200s, B200s) to optimize voice model inference.\n Collaborate with model partners (Cartesia, Deepgram, Rime, and others) to bring their models to production on Together's infrastructure.\n Build quality evaluation frameworks that guide model selection for customers and inform the roadmap.\n Join a small, early-stage team with outsized impact on a fast-growing product area.\n \n Responsibilities \n \n Optimize inference performance for voice models (STT, TTS, speech-to-speech) — targeting best-in-class TTFB, throughput, and GPU utilization across our curated model set.\n Productionize voice models on serverless and dedicated endpoints, including batching strategies, streaming inference, and memory management tailored to audio workloads.\n Build and maintain a voice model evaluation framework — measuring WER across accents, languages, and noise conditions for STT; naturalness, latency, and pronunciation accuracy for TTS.\n Enable new model architectures in our serving stack as the field evolves, including audio-native LLMs, codec-based models (SNAC), and speech-to-speech systems.\n Collaborate with model partners to integrate and optimize their models (Cartesia, Deepgram, Rime, and others) running on Together's infrastructure.\n Profile and debug performance across the full inference stack — from GPU kernels to framework-level bottlenecks — and ship measurable improvements.\n Work with the platform engineering side of the team to ensure the serving layer meets the latency and reliability requirements of real-time voice APIs.\n Contribute to voice model fine-tuning capabilities (STT and TTS) as we enable customers to build differentiated voice experiences on Together.\n Lay the groundwork for multiple new products down the line.\n \n Requirements \n \n 5+ years of experience in ML engineering, with a focus on model serving, inference optimization, or ML infrastructure.\n Hands-on experience with LLM serving engines (vLLM, SGLang, TensorRT-LLM, or similar) — comfortable reading and modifying engine internals, not just using APIs.\n Strong proficiency in Python and PyTorch; experience with GPU profiling and optimization (CUDA, memory management, kernel-level debugging).\n Track record of shipping ML systems to production with measurable performance improvements.\n Strong product sense — you think about what developers building voice apps actually need, not just what's technically interesting.\n Comfort working on a small, early-stage team where you'll wear multiple hats and move fast.\n Experience with speech and audio ML (ASR, TTS architectures, audio signal processing) is a strong plus but not required — you can learn this quickly if you have strong ML engineering fundamentals.\n Familiarity with audio codecs and tokenization schemes (SNAC, Encodec, DAC) is a plus.\n Experience training or fine-tuning speech models is a plus.\n Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field, or equivalent practical experience\n \n About Together AI \n Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.\n Compensation \n We offer competitive compensation, start","salary_min":200000,"salary_max":260000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["gpu","fine-tuning","mlops","pytorch","speech","llm","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5088817007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-03-30T19:36:00Z","expires_at":"2026-06-29T14:01:49.607345Z","created_at":"2026-04-13T09:37:38.250213Z","updated_at":"2026-05-30T14:01:49.718548Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/fdeb7783-851b-48d1-810b-3d39970161b6"},{"id":"6bdbfae6-b541-4956-99e1-7b2487292755","company_id":"7551b4ca-b2b0-493a-ab58-a15bd9c50393","title":"Senior Machine Learning Engineer - Voice Experience ","slug":"senior-machine-learning-engineer-automatic-speech-recognition-asr-31bbe25d","description":"Cresta unlocks the true potential of the customer experience, turning every conversation into a competitive advantage. Cresta’s unified AI platform combines conversational AI agents, real-time human agent augmentation, and comprehensive conversation intelligence to drive revenue and efficiency gains across every channel. The world’s leading companies, including United Airlines, Cox Communications, and Marriott, use Cresta to power world-class customer experiences every day. \n Born from the Stanford AI Lab, Cresta has raised more than $270 million from the world’s leading investors, including a16z, Greylock, and Sequoia. Cresta’s leadership includes some of the leading minds in AI today. Our CEO, Ping Wu , founded and led Google's Contact Center AI and Vertex AI platforms before joining Cresta to build the future of AI-driven customer experiences.\n Over the next few years, AI is going to redefine how people all over the world interact with businesses every day. Come build that future at Cresta.\n About the role \n \n We are looking for a Senior Machine Learning Engineer, Voice Experience to help build the next generation of AI-powered voice systems for the contact center. In this role, you will work at the intersection of speech, language, and real-time production systems , improving how AI listens, understands, reasons, empathizes, and responds in live customer conversations. \n You will develop and improve machine learning systems that power voice experiences end to end, including automatic speech recognition, turn detection, downstream language understanding, retrieval-augmented and agentic workflows, quality measurement, text to speech, and production optimization . You will partner closely with applied researchers, product managers, designers, forward deployed engineers, and platform engineers to ensure model and system improvements translate into measurable customer and business impact.\n This role is ideal for someone who is excited by both model quality and production reality : designing rigorous evaluation frameworks, analyzing failure modes, improving latency and robustness, and shipping systems that perform reliably at scale in real-time voice environments.\n Responsibilities \n \n \n Design, train, evaluate, and deploy machine learning systems that power real-time voice experiences, including ASR, speech understanding, turn detection, text to speech, speech to speech, classification, entity extraction, summarization, and structured insight generation.\n Improve the quality of voice AI systems through error analysis, data curation, metric design, benchmarking, and iterative model improvement, with a strong focus on real-world performance.\n Build evaluation frameworks for complex voice and agentic systems, measuring metrics such as accuracy, robustness, latency, faithfulness, naturalness, professionalism, task completion, and cost.\n Diagnose and mitigate failure modes across the voice stack, including transcription errors, hallucinations, retrieval failures, tool misuse, prompt brittleness, context drift, and multi-step reasoning breakdowns.\n Design and optimize low-latency ML workflows for live conversations, balancing model quality with system responsiveness, scalability, and reliability.\n Partner with platform and backend engineers to productionize real-time inference, streaming pipelines, quality monitoring, and continuous model iteration.\n Collaborate cross-functionally with product, design, frontend, and backend teams to integrate voice intelligence seamlessly into Cresta’s platform.\n Establish best practices for offline evaluation, online experimentation, model validation, observability, and ongoing quality monitoring in production.\n Mentor engineers, contribute to technical strategy, and help shape the roadmap for Cresta’s voice AI systems.\n \n Qualifications We Value \n  \n \n Bachelor’s degree in Computer Science, Mathematics, Machine Learning, AI, or a related field; Master’s or Ph.D. preferred.\n 5+ years of experience building, evaluating, and deploying machine learning systems in production.\n Strong background in one or more of the following: speech recognition, speech processing, NLP, generative AI, or conversational AI .\n Deep experience with model evaluation, benchmarking, error analysis, and quality improvement for production ML systems.\n Strong expertise with modern ML frameworks and tooling such as PyTorch, TensorFlow, and Hugging Face.\n Solid understanding of transformer-based models, embeddings, retrieval systems, and large-scale training or inference workflows.\n Experience designing and deploying real-time ML systems with strong requirements around latency, scalability, and reliability.\n Experience building data pipelines and tooling for experimentation, measurement, and large-scale quality analysis.\n Ability to work across research and engineering boundaries and translate promising ideas into production-grade systems.\n Strong communication and technical leadership skills","salary_min":205000,"salary_max":270000,"location":"United States","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","rag","nlp","speech","agents","data-pipeline","llm","generative-ai"],"apply_url":"https://job-boards.greenhouse.io/cresta/jobs/5155675008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-03-17T14:31:23Z","expires_at":"2026-06-29T14:04:04.826598Z","created_at":"2026-04-13T09:39:51.929183Z","updated_at":"2026-05-30T14:04:04.936305Z","company_name":"Cresta","company_slug":"cresta","company_logo_url":"https://www.google.com/s2/favicons?domain=cresta.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6bdbfae6-b541-4956-99e1-7b2487292755"},{"id":"d10ccac5-6174-4435-a8ab-164876a1a197","company_id":"37683849-8d19-405f-a4f6-9015ab2a4fed","title":"Agent Designer","slug":"agent-designer-40361e55","description":"PolyAI automates customer service through lifelike voice assistants that let customers lead a conversation. Our voice assistants make it possible for businesses to deliver outstanding customer service that rivals their human agents. Our customers, which include the world’s leading logos, are expanding how they use our platform, driving automation of critical customer service operations and integrating PolyAI into their daily customer service workflows.  \n PolyAI is on the hunt for exceptional Agent Designers! PolyAI builds enterprise voice AI agents that handle millions of customer conversations for global brands, delivering conversational experiences so natural they feel like talking to a real person. This is a role for people who want to own projects end-to-end and create conversational experiences that will be heard by millions of customers worldwide. This is suitable for people who are looking to start their career - new graduates welcome! \n Responsibilities: \n \n Designing the customer experience of AI agents, from simple FAQ answers to long and complex conversations \n Implementing and maintaining agents using our own platform, Agent Studio, as well as Python and other tools \n Obsessing over metrics to assess performance and customer satisfaction - making sure everything you do is measurable and having a big impact \n Listening to how real customers interact with your agents, and prioritising the right things to make each interaction even better \n Helping our engineering team ensure that our implementations of Generative AI are effective, accurate, and safe \n \n Minimum Requirements: \n \n BS/MS degree in Interaction Design, Linguistics, Human-Computer Interaction, Artificial Intelligence, Computer Science, or another related field \n Foundational Python knowledge: variables, lists, loops, conditionals, and functions \n Experience working with large language models (LLMs) and Generative AI \n Strong focus on creating great user experiences, with meticulous attention to detail and consistency \n Ability to explain and distill complex ideas using clear and inclusive language \n Ability to craft conversational and UX copy that guides users naturally through their journeys, reducing friction at every step \n \n Nice to Have: \n \n Knowledge of or aptitude for analytics or data science, including data manipulation, analysis, and visualisation \n Experience managing internal and external stakeholders through various stages of content development \n Previous experience in conversation design/AI agent development \n Experience collaborating with cross-functional teams \n \n We provide a competitive salary range for this role - which is $80,000 – $90,000 CAD depending on level and experience. Please note this range is intended as a guide, not a guarantee. Final compensation will be based on individual qualifications, relevant experience, and the scope of the role. This role also includes equity, giving you the opportunity to share in the long-term success of the business. \n The listed expectations reflect what we're hiring for, so we encourage you to review the job description carefully. \n \n Benefits \n 💰 Participation in the company’s employee share options plan \n 🏝 Flexible PTO policy \n 📚 Annual learning and development allowance: We will reimburse the costs of any certified and non-certified training, including conferences, events, books and subscriptions that are relevant to your role at PolyAI, in addition to any formal training that the company offers \n 🏡 We’re all about making WFH work for you - that’s why we offer a one-off WFH allowance when you join. Offering perks like noise-cancelling headphones or a comfortable desk chair to boost your comfort and focus! \n 🏥 Healthcare plan: We offer health insurance through Allianz. Full details on the plan will be shared with you on your first day \n 🌎 Sabbatical Program: 5-week paid sabbatical available after 5 years of employment \n \n At PolyAI, we take great pride in our values - they guide everything we do. We believe that a strong culture leads to meaningful work and lasting impact. \n Our core values are: \n Only the best We expect the best from our people, we hire people that expect the best from themselves, and we nurture this drive for excellence. \n Ownership We care deeply about what we do. We take ownership of our initiatives, decisions and outcomes. \n Relentlessly improve We demand more from ourselves and are always evolving. Continuous, obsessive improvement is the only way we will transform the world of conversational AI. \n Bias for action Our world moves quickly and so do we. We take calculated risks and we deliver impact fast. \n Disagree and commit We are all working toward the same goal. If we donʼt agree with something, we work hard to understand it and when a decision is made, we accept it and give it our all. \n Build for people We want the world to enjoy the experiences they have with us. We are building for a future that prefers automat","salary_min":80000,"salary_max":90000,"location":"Canada","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["llm","healthcare","agents","speech","generative-ai"],"apply_url":"https://job-boards.eu.greenhouse.io/polyai/jobs/4802138101","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-03-09T10:52:59Z","expires_at":"2026-06-29T14:08:00.234413Z","created_at":"2026-04-13T09:44:33.237775Z","updated_at":"2026-05-30T14:08:00.352607Z","company_name":"PolyAI","company_slug":"polyai","company_logo_url":"https://www.google.com/s2/favicons?domain=poly.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d10ccac5-6174-4435-a8ab-164876a1a197"},{"id":"e8efc15c-a683-4c48-bb7a-efc808167bc6","company_id":"e484db67-07a8-4412-8205-29832db55b17","title":"Machine Learning Engineer","slug":"machine-learning-engineer-d3bf99b4","description":"The Opportunity Do you want to lead projects to build and deploy cutting-edge AI technology to help people get unparalleled value from meetings and conversations? Join our core AI team responsible for ML and work alongside industry-veteran scientists and engineers. As a Machine Learning Engineer, you’ll bring your strong software engineering mindset to machine learning in order to scale and optimize our ML systems—creating and transforming innovative research into production-ready features that power Otter’s summarization and conversational intelligence products.\n Your Impact \n \n Architect, build, and evolve large-scale SID / ASR / NLP / LLM systems that power mission-critical product experiences including summarization, chat, and speech understanding across millions of conversations.\n Lead the design and implementation of training, fine-tuning, post-training, and inference strategies for large language and speech models using PyTorch and/or JAX, making principled trade-offs across quality, latency, cost, and reliability.\n Design and improve model architectures, loss functions, decoding strategies, and training techniques for speech and language models, informed by both research and production constraints.\n Own end-to-end ML system lifecycles , from research prototyping through production deployment, monitoring, iteration, and long-term maintenance.\n Partner deeply with product, and infrastructure teams to develop and translate cutting-edge research into scalable, production-grade systems that deliver measurable user and business impact.\n Drive system-level improvements in model performance, robustness, observability, and operational excellence using real-world conversational data at scale.\n Set technical direction and best practices for ML infrastructure, data pipelines, evaluation frameworks, and deployment workflows in a cloud environment.\n Identify and resolve complex, ambiguous problems in model behavior, data quality, scaling, and system interactions, often before they surface as user-visible issues.\n Mentor and elevate other engineers , influencing team standards, reviewing designs, and contributing to a culture of strong technical decision-making and execution.\n \n We're Looking for Someone Who \n \n Holds a Bachelor’s or Master’s degree in Computer Science or a related field with 3+ years of relevant industry experience ; PhD is preferred.\n Has deep, hands-on experience building, fine-tuning, and post-training large language models or other foundation models, including an understanding of failure modes and trade-offs.\n Demonstrates strong command of modern ML research , with the ability to critically evaluate new papers and decide what is production-worthy versus experimental.\n Has interest in creating innovation and advancing applied research \n Has extensive experience deploying, monitoring, and operating ML systems in production , including model versioning, rollback strategies, and performance regression detection.\n Is comfortable working with large-scale speech and conversational datasets , including data preprocessing, augmentation, quality analysis, and labeling strategies to support model training and evaluation.\n Has experience scaling ML systems across training, inference, and serving infrastructure while balancing cost, latency, and reliability constraints.\n Is highly effective at cross-functional collaboration , working end-to-end with product, infra, research, and data teams to deliver outcomes—not just models.\n Can lead technical projects independently , driving clarity in ambiguous problem spaces and making sound architectural decisions.\n Has experience with or strong interest in agentic systems, tool-use frameworks, or multi-model orchestration .\n Has significant experience with at least one of the following areas: (1) Speech recognition (ASR), (2) Text-to-speech (TTS), (3) Multimodal (speech/text) foundation models, or (4) modern LLM NLP tasks (e.g., summarization, dialogue, speech understanding), especially in real-world production settings.\n Experience with personalization, recommendation systems, or user modeling is a plus\n \n About Otter.ai \n We are in the business of shaping the future of work. Our mission is to make conversations more valuable.\n With over 1B meetings transcribed, Otter.ai is the world’s leading tool for meeting transcription, summarization, and collaboration. Using artificial intelligence, Otter generates real-time automated meeting notes, summaries, and other insights from in-person and virtual meetings - turning meetings into accessible, collaborative, and actionable data that can be shared across teams and organizations. The company is backed by early investors in Google, DeepMind, Zoom, and Tesla.\n Otter.ai is an equal opportunity employer. We proudly celebrate diversity and are dedicated to inclusivity. \n \n *Otter.ai does not accept unsolicited resumes from 3rd party recruitment agencies without a written agreement in place for permanent placeme","salary_min":196000,"salary_max":221000,"location":"Mountain View, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["agents","generative-ai","pytorch","data-pipeline","speech","llm","nlp","fine-tuning"],"apply_url":"https://otter.ai/careers?gh_jid=7634875003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-02-18T01:02:38Z","expires_at":"2026-06-29T14:13:41.459264Z","created_at":"2026-04-16T15:56:00.563276Z","updated_at":"2026-05-30T14:13:41.570417Z","company_name":"Otter.ai","company_slug":"otter-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=otter.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e8efc15c-a683-4c48-bb7a-efc808167bc6"},{"id":"014d817c-67ed-4e2e-9809-bc16a189f91d","company_id":"1bb3dab4-a54d-456f-bfd6-ad41828ae177","title":"Senior Manager (AI Engineering)","slug":"senior-manager-ai-engineering-d4c31687","description":"What we want to accomplish and why we need you \n Suki is creating a new category in the health-tech space: Ambient Clinical Intelligence. Our AI-native products and platforms help power clinical workflows via ambient presence. What does that mean? Currently, doctors use electronic health record systems to take notes on patient encounters. This is a digital version of the paper charts that you may have seen in your doctor’s office or on TV. These systems can be hard to navigate and time-consuming to manage. Doctors would rather spend that time with patients. We are creating the solution using speech recognition, generative AI, and systems integration. Doctors that use Suki already spend over 70% less time on administrative tasks, and we’re striving to do even better. Come and join us!\n We are a user-driven company and are committed to making sure every pixel of our product is in service of the doctor. We’re a team of technologists, clinicians, and industry experts working together to push the limits on technology used in medicine. We’re confident enough to move fast and talented enough not to break things. Check out this short video to learn more about our mission and our culture.\n Our tech stack includes GCP, Kubernetes, Golang, Python, React, TypeScript, JavaScript, Swift, Kotlin, gRPC, and GraphQL.\n What you will do every day \n As a Senior Engineering Manager for our AI Engineering team, you will serve as the strategic leader and mentor for our team of SF Bay Area based Machine Learning and Backend Engineers developing new Clinical Intelligence features alongside customers and partners.  You will act as the bridge between ambitious product vision and technical execution, partnering closely with Product, Design, and Executive leadership to prioritize and execute on a roadmap that redefines the category. \n This role is responsible for project delivery from the ML data pipeline, to model and agent development, and the services/interfaces that will power our mobile and web clients, and public facing APIs.  These services primarily perform inference, summarization, classification and transformation tasks.  Previous experience using semantic, agentic, and structured RAG is expected.  Candidates also need to have a strong background in software engineering and operations, and experience deploying high-concurrency, model-backed microservices atop Kubernetes.\n Given the rate of industry growth and change, the role requires staying up to date with technological advancements in AI, and suggesting \u0026 evaluating system design and inference approaches proposed by the team.  You will build and iterate on features and services that directly impact thousands of clinicians, and improve their lives everyday\n Ok, you're sold, but what are we looking for in the perfect candidate? \n \n Expertise: Hands-on architectural and procedural experience working with ML-based systems and applications.  Prior tech lead experience mandatory.\n Action oriented: Understands how perfect can be the enemy of good, and adjusts system design and staffing to best fit the functional and business objective.\n Creativity: Applies new algorithmic, agentic and fine-tuned model techniques as necessary, thinks through system design alternatives with the team to balance between good, fast, cheap.\n Humility: Collaborates with engineering leaders and senior ICs to build a collaborative culture across geographies where roles and responsibilities are understood and respected.\n Problem solving: Works on multiple tasks and prioritizes responsibilities within an Agile/Scrum environment.  Partners well with Product and GTM teams.\n Ownership: Understands production systems, deployments and ensures application performance and uptime.\n Craftsmanship: Maintains high standards of code quality through evangelisation of unit testing, integration tests, and user experience.\n \n Requirements:  \n \n 7+ years of total experience in Software Engineering management, with at least 3 years specifically leading high-performing teams of ML/DS engineers in a fast-paced SaaS environment.\n Technical fluency in NLP and ASR, specifically in the context of ambient sensing, large language models (LLMs), or agentic architectures.\n A proven track record of overseeing the full end-to-end lifecycle of multiple enterprise-grade model-driven services from research to large-scale production deployment.\n Ability to guide the team in building robust, distributed backend services for AI applications.\n Strong experience in MLOps at scale, ensuring the team has the tooling and infrastructure to iterate quickly and ship reliably.\n An advanced degree in Computer Science or a related field, with a rigorous grasp of algorithms, data structures, and the ability to review complex code in Golang or Python.\n Exceptional ability to translate ambiguous business requirements into concrete technical designs that drive user impact in the healthcare space.\n Excellent written/verbal communication, int","salary_min":265000,"salary_max":300000,"location":"Redwood City, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","llm","healthcare","speech","api-design","agents","generative-ai","rag"],"apply_url":"https://www.suki.ai/open-positions?gh_jid=7618116003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-02-04T02:22:26Z","expires_at":"2026-06-29T14:11:34.200812Z","created_at":"2026-04-16T11:18:08.305811Z","updated_at":"2026-05-30T14:11:34.312415Z","company_name":"Suki","company_slug":"suki","company_logo_url":"https://www.google.com/s2/favicons?domain=suki.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/014d817c-67ed-4e2e-9809-bc16a189f91d"},{"id":"3c5c8f81-2bfe-48f7-b375-db25452867ab","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer/Research Scientist, Audio","slug":"research-engineerresearch-scientist-audio-6b225ea4","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n Anthropic’s Audio team pushes the boundaries of what's possible with audio with large language models. We care about making safe, steerable, reliable systems that can understand and generate speech and audio, prioritizing not only naturalness but also steerability and robustness. As a researcher on the Audio team, you'll work across the full stack of audio ML, developing audio codecs and representations, sourcing and synthesizing high quality audio data, training large-scale speech language models and large audio diffusion models, and developing novel architectures for incorporating continuous signals into LLMs.\n Our team focuses primarily but not exclusively on speech, building advanced steerable systems spanning end-to-end conversational systems, speech and audio understanding models, and speech synthesis capabilities. The team works closely with many collaborators across pretraining, finetuning, reinforcement learning, production inference, and product to get advanced audio technologies from early research to high impact real-world deployments.\n You may be a good fit if you: \n \n Have hands-on experience with training audio models, whether that's conversational speech-to-speech, speech translation, speech recognition, text-to-speech, diarization, codecs, or generative audio models\n Genuinely enjoy both research and engineering work, and you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other\n Are comfortable working across abstraction levels, from signal processing fundamentals to large-scale model training and inference optimization\n Have deep expertise with JAX, PyTorch, or large-scale distributed training, and can debug performance issues across the full stack\n Thrive in fast-moving environments where the most important problem might shift as we learn more about what works\n Communicate clearly and collaborate effectively; audio touches many parts of our systems, so you'll work closely with teams across the company\n Are passionate about building conversational AI that feels natural, steerable, and safe\n Care about the societal impacts of voice AI and want to help shape how these systems are developed responsibly\n \n Strong candidates may also have experience with: \n \n Large language model pretraining and finetuning\n Training diffusion models for image and audio generation\n Reinforcement learning for large language models and diffusion models\n End-to-end system optimization, from performance benchmarking to kernel optimization\n GPUs, Kubernetes, PyTorch, or distributed training infrastructure\n \n Representative projects: \n \n Training state-of-the art neural audio codecs for 48 kHz stereo audio\n Developing novel algorithms for diffusion pretraining and reinforcement learning\n Scaling audio datasets to millions of hours of high quality audio\n Creating robust evaluation methodologies for hard-to-measure qualities such as naturalness or expressiveness\n Studying training dynamics of mixed audio-text language models\n Optimizing latency and inference throughput for deployed streaming audio systems\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $350,000 — $500,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application","salary_min":350000,"salary_max":500000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"principal","tags":["distributed-systems","pre-training","search","pytorch","llm","diffusion-models","speech","reinforcement-learning"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5074815008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-01-16T22:10:35Z","expires_at":"2026-06-29T14:00:22.019247Z","created_at":"2026-04-13T09:36:00.566717Z","updated_at":"2026-05-30T14:00:22.12651Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3c5c8f81-2bfe-48f7-b375-db25452867ab"},{"id":"e3fc25fa-dd20-4b24-9238-9e1b9ab7110f","company_id":"dc461dc0-20f9-4429-a009-6cb473fc466c","title":"Senior Software Engineer, Go - LLM Team","slug":"senior-software-engineer-go-llm-team-e5be9a63","description":"Why AssemblyAI \n AssemblyAI builds the best-in-class Voice AI models powering the next generation of voice applications. Our models serve 600M+ inference calls monthly, process 1M+ hours of audio daily, and power 2 billion+ end-user experiences. The Voice AI space is at an inflection point; we’re looking for folks truly excited to join a small team and help define the future of the industry.\n We are one of the most capital-efficient AI companies on the planet - with under 100 people generating roughly $500K ARR per employee, we sit among the top 5 most revenue-dense teams within the fastest-growing AI companies today. That's not an accident; it's a deliberate choice to stay lean, move fast, and give every person on the team outsized ownership and impact. With thousands of customers including Granola, Fireflies, Figure AI, and CallRail, the company has real scale - processing over 2 million hours of audio daily and handling more than 1 million API calls every day. This is a rare growth-stage opportunity where the business is proven and the trajectory is steep, but the team is still small enough that your fingerprints are on everything.\n If you've ever felt buried under layers of bureaucracy, starved of real ownership, or frustrated watching your work disappear into a slow-moving org, AssemblyAI is built differently. The company operates as a true meritocracy, with no heavy planning or approval processes and no gatekeeping on the tools or information you need. For anyone who genuinely cares about voice AI, not as a trend to chase, but as a technology to build,  this is the place where the most interesting problems at the most interesting scale are being solved by a team small enough that you'll actually know everyone's name.\n We’re committed to creating a space where our employees can bring their full selves to work and have equal opportunity to succeed. No matter your race, gender identity or expression, sexual orientation, religion, origin, ability, age, veteran status, if joining this mission speaks to you, we encourage you to apply!\n About the Role \n We're seeking an exceptional Senior Software Engineer to join our LLM team. This role is focused on building and maintaining our LLM gateway service—a unified API platform that connects customers to multiple LLM providers. You'll work on high-impact projects that directly solve customer problems, improve their AI and agentic workflows, and ensure reliable access to the best models for their use cases.\n This is a deeply customer-focused role. You'll work closely with our customer success team to understand customer challenges, help optimize their prompt strategies, build features that address their pain points, and ensure our service reliably delivers value. We're looking for someone who combines software engineering excellence with genuine curiosity about how customers use AI and a drive to make their lives better.\n As a Senior Engineer, you'll drive technical execution within the team, taking ownership of significant features and integrations while mentoring more junior engineers. You should be passionate about writing clean, maintainable code, implementing comprehensive testing strategies, and building highly reliable systems in service of solving real customer problems.\n This role requires close collaboration with customer success, product managers, external API providers, and other engineering stakeholders. You'll need to balance technical excellence with pragmatic delivery in a fast-paced startup environment where uptime, reliability, and customer success are critical.\n What You'll Do \n Solve Customer Problems \n \n Partner closely with the customer success team to understand customer use cases, challenges, and integration needs\n Translate customer pain points surfaced by the CS team into technical solutions and product improvements\n Build features and tooling that directly address customer needs and improve their workflows\n Provide technical guidance and expertise to the customer success team to help them support customers effectively\n \n Drive Technical Execution \n \n Own and deliver complete features and integrations within our LLM gateway service\n Build and maintain integrations with multiple LLM providers and AI services (OpenAI, Anthropic, Google Vertex, AWS Bedrock etc.)\n Write clean, maintainable, well-tested code following best practices\n Design and implement scalable, fault-tolerant solutions with appropriate abstractions\n Proactively identify and address technical debt, reliability issues, and code quality concerns\n Participate in on-call rotation to ensure service reliability and rapid incident response\n \n Elevate Engineering Standards \n \n Conduct thorough code reviews focused on maintainability, testing, reliability, and architectural concerns\n Ensure proper test coverage across unit, integration, and end-to-end testing levels\n Improve code maintainability and extensibility through targeted refactoring\n Contribute to runbooks, incident po","salary_min":180000,"salary_max":240000,"location":"United States","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["agents","cloud","speech","distributed-systems","llm","api-design","go"],"apply_url":"https://job-boards.greenhouse.io/assemblyai/jobs/4638677005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-12-10T22:02:59Z","expires_at":"2026-06-29T14:04:37.264416Z","created_at":"2026-04-13T09:40:31.969973Z","updated_at":"2026-05-30T14:04:37.376953Z","company_name":"AssemblyAI","company_slug":"assemblyai","company_logo_url":"https://www.google.com/s2/favicons?domain=assemblyai.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e3fc25fa-dd20-4b24-9238-9e1b9ab7110f"},{"id":"862b6d52-7f6e-451e-9631-3951ac1d6839","company_id":"4d985fa4-b897-4f93-9745-c332367ad86b","title":"Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-2030adc8","description":"ABOUT RETELL AI\n\nRetell AI is using first principles to reimagine the call center with cutting-edge voice AI.\n\nThousands of companies now utilize Retell’s AI voice agents to handle sales, support, and logistics calls that once required large teams of human agents. Backed by Y Combinator, Alt Capital, and other leading investors, we have scaled to $60M ARR with a team of 40 people, up from $5M at the start of 2025.\n\nOur vision for 2026 is to build a modern CX platform where entire contact centers are powered by AI. Instead of basic automation that needs constant human tuning, we’re creating intelligent AI “workers” that can act as frontline agents, QA analysts, and managers — continuously executing, monitoring, and improving customer interactions.\n\nWe’re growing quickly and looking for ambitious builders who want to tackle hard technical problems, move fast, and have real impact at one of the fastest-growing voice AI startups.\n\nLet’s build the future together.\n\n - We’re a top 50 AI app in a16z list: https://tinyurl.com/5853dt2x\n\n - #4 on Brex's Fast-Growing Software Vendors of 2025: https://www.brex.com/journal/brex-benchmark-december-2025\n\n - We're also one of the top ranking startups on: https://leanaileaderboard.com/\n\n - Enterprise tech 30: https://www.wing.vc/et30/overview\n\n\n\nABOUT THE ROLE\n\nThis is a hands-on, high-ownership role for ML engineers who want to build production models that actually ship, and perform under real-world constraints. As a Founding Senior Machine Learning Engineer at Retell, you’ll work across the ML stack to power human-like voice agents that handle millions of real-time phone conversations.\n\nYou’ll fine-tune large language models and audio models, evaluate them with rigorous benchmarks (and human feedback), and deploy them into latency-sensitive, high-traffic systems. You’ll own model performance end-to-end—from training pipelines to post-deployment monitoring—and shape our ML strategy alongside the founding team.\n\nIf you’re excited by hard technical challenges, fast iteration, and the opportunity to define how voice AI works at scale, this role is a rare chance to do it from the ground up.\n\n\n\nKEY RESPONSIBILITIES\n\n - Train \u0026 Tune Models – Fine-tune LLMs and audio models to maximize speed, accuracy, and production-readiness—pushing the frontier of real-time AI voice experiences.\n\n - Benchmark \u0026 Evaluate – Build datasets, define rigorous metrics, and measure model performance across high-impact voice AI tasks to guide development.\n\n - Deploy to Production – Work closely with engineering to ship models, monitor them in the wild, and ensure they stay fast, reliable, and accurate at scale.\n\n - Run Human Evaluations – Build scalable pipelines to collect structured human feedback, benchmark subjective quality, and inform model iterations.\n\n - Level Up Infrastructure – Design and maintain the ML infrastructure needed for fast experimentation, robust training, and continuous deployment.\n   \n\nYOU MIGHT THRIVE IF YOU\n\n - ML Engineer with Real-World Experience – You’ve trained and shipped models in production. Bonus if you’ve worked with LLMs or audio models.\n\n - Fluent in Modern ML Stack – You know your way around Python, PyTorch, and today’s ML tools—from training pipelines to evaluation benchmarks.\n\n - Execution-Oriented – You move fast, take ownership, and focus on solving real problems over perfect ones.\n\n - Startup-Ready – You’re adaptable, resilient, and energized by ambiguity and fast-changing priorities.\n\n - Clear Communicator \u0026 Team Player – You collaborate well across functions and push decisions forward.\n   \n\nJOB DETAILS\n\n - Cash: $225,000 - $325,000 base salary\n\n - Equity: Offers Equity\n\n - Location: Redwood City, CA, US\n\n - US Visas: Retell AI is open to sponsoring work authorization for qualified candidates, including H1B/H-1B, TN, L-1, E-3, F-1 (OPT/CPT), and O-1 visas.\n   \n\nOTHER BENEFITS\n\n - 100% coverage for medical, dental, and vision insurance\n\n - $70/day DoorDash credit for unlimited breakfast, lunch, dinner, and snacks\n\n - $200/month wellness reimbursement (gym, fitness classes, etc.)\n\n - $300/month commuter reimbursement (gas, Caltrain, etc.)\n\n - $75/month phone bill reimbursement\n\n - $50/month internet reimbursement\n   \n\nCOMPENSATION PHILOSOPHY\n\n - Best Offer Upfront: Choose from three cash-equity balance options, no negotiation needed.\n\n - Top 1% Talent: Above-market pay (top 5 percentile) to attract high performers.\n\n - High Ownership: Small teams, \u003e$1M revenue/employee, and significant equity.\n\n - Performance-Based: Offers tied to interview performance, not experience or past salaries.\n   \n\nINTERVIEW PROCESS\n\n - Talent Screen (15min): chat with our recruiter to get a better sense of the role, the team, and what it’s like to work here.\n\n - Technical Interview (45 min): LLM theory specific coding Interview(Pytorch)\n\n - Technical Interview (45 min): Live Practical Systems Design and Coding Interview.\n\n","salary_min":225000,"salary_max":325000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["llm","speech","pytorch","machine-learning"],"apply_url":"https://jobs.ashbyhq.com/retell-ai/dcc921b7-fccc-459a-93c2-10adb4aa147a/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-10-16T03:11:50.217Z","expires_at":"2026-06-29T14:11:20.275565Z","created_at":"2026-04-16T11:17:45.347929Z","updated_at":"2026-05-30T14:11:20.388858Z","company_name":"Retell AI","company_slug":"retell-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=retellai.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/862b6d52-7f6e-451e-9631-3951ac1d6839"},{"id":"7d188877-64a8-4be4-aff5-4d0379313cf2","company_id":"dc461dc0-20f9-4429-a009-6cb473fc466c","title":"Senior Security Operations Engineer","slug":"senior-security-operations-engineer-0321ca6d","description":"Why AssemblyAI \n AssemblyAI builds the best-in-class Voice AI models powering the next generation of voice applications. Our models serve 600M+ inference calls monthly, process 1M+ hours of audio daily, and power 2 billion+ end-user experiences. The Voice AI space is at an inflection point; we’re looking for folks truly excited to join a small team and help define the future of the industry.\n We are one of the most capital-efficient AI companies on the planet - with under 100 people generating roughly $500K ARR per employee, we sit among the top 5 most revenue-dense teams within the fastest-growing AI companies today. That's not an accident; it's a deliberate choice to stay lean, move fast, and give every person on the team outsized ownership and impact. With thousands of customers including Granola, Fireflies, Figure AI, and CallRail, the company has real scale - processing over 2 million hours of audio daily and handling more than 1 million API calls every day. This is a rare growth-stage opportunity where the business is proven and the trajectory is steep, but the team is still small enough that your fingerprints are on everything.\n If you've ever felt buried under layers of bureaucracy, starved of real ownership, or frustrated watching your work disappear into a slow-moving org, AssemblyAI is built differently. The company operates as a true meritocracy, with no heavy planning or approval processes and no gatekeeping on the tools or information you need. For anyone who genuinely cares about voice AI, not as a trend to chase, but as a technology to build,  this is the place where the most interesting problems at the most interesting scale are being solved by a team small enough that you'll actually know everyone's name.\n We’re committed to creating a space where our employees can bring their full selves to work and have equal opportunity to succeed. No matter your race, gender identity or expression, sexual orientation, religion, origin, ability, age, veteran status, if joining this mission speaks to you, we encourage you to apply!\n About the role: \n AssemblyAI runs a mature, multi-framework security and compliance program—including SOC 2 (all trust criteria), ISO 27001, and PCI 4.0—that protects the infrastructure and customer data behind our industry-leading Voice AI API. We're hiring a Senior Security Operations Engineer to join our IT \u0026 Security team as the company's first security engineering role.\n This role sits at the intersection of security engineering and security operations. You'll split your time between hands-on engineering work—threat modeling, secure code reviews, security tooling, and infrastructure hardening alongside our platform and product engineering teams—and the operational work that keeps our security program running: compliance audit cycles, vulnerability management, customer questionnaires, and monitoring. You should be energized by both sides of that equation, not just one.\n This is a high-ownership role on a small team. You'll work closely with engineers across the company, partner with sales and legal on customer-facing security needs, and have a direct hand in shaping how AssemblyAI secures its products, infrastructure, and internal tools—including a rapidly growing landscape of agentic AI development.\n What You’ll Do: \n Security Engineering \n \n Conduct threat modeling and security design reviews for new features, services, and architectural changes—partnering with product and platform engineers early in the design phase.\n Perform secure code reviews and provide actionable feedback, focusing on authentication, authorization, input handling, secrets management, and data protection.\n Deploy and maintain security tooling across the development lifecycle—SAST, SCA, DAST, secret scanning, IaC scanning, and CI/CD security guardrails.\n Support best practices to adopt secure-by-default libraries, frameworks, paved-road patterns, and developer guidance to reduce classes of vulnerabilities across the codebase.\n Partner with platform engineering on infrastructure and environment security—including AWS resource hardening, Terraform-managed infrastructure reviews, network segmentation, and environment isolation improvements.\n Contribute to incident response for security events: investigation, root cause analysis, and post-incident hardening.\n \n Security Operations \n \n Drive vulnerability triage and prioritization across teams, tracking remediation against targets and reporting metrics. Step in to remediate directly through patches and PRs where you identify high-impact opportunities.\n Partner with sales and legal responding to customer and vendor questionnaires, RFP security sections, and trust-and-safety inquiries.\n Support SOC 2, ISO 27001, PCI 4.0, and other compliance audit cycles by gathering evidence, documenting controls, and coordinating with auditors.\n Monitor and respond to alerts from endpoint, cloud, and application security tools; manage vulnerability","salary_min":180000,"salary_max":220000,"location":"Remote","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["llm","code-generation","speech","agents","cloud","security"],"apply_url":"https://job-boards.greenhouse.io/assemblyai/jobs/4699429005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-26T19:57:54Z","expires_at":"2026-06-29T14:04:37.178948Z","created_at":"2026-05-27T14:04:46.450634Z","updated_at":"2026-05-30T14:04:37.294979Z","company_name":"AssemblyAI","company_slug":"assemblyai","company_logo_url":"https://www.google.com/s2/favicons?domain=assemblyai.com\u0026sz=128","quality_score":85,"url":"https://aidevboard.com/job/7d188877-64a8-4be4-aff5-4d0379313cf2"},{"id":"e4947f7b-32bf-402a-911d-5b9dc7fe363e","company_id":"7dce760b-ac61-4857-866a-ebb698f93440","title":"Head of Agent Relations","slug":"head-of-agent-relations-006c11b6","description":"About SIMBA Voice Agents by Speechify SIMBA lets companies deploy natural, human-sounding AI voice agents in 71+ languages with sub-second latency. We don't just hand customers a product and wish them luck — every customer gets a Forward Deployed Engineer who builds their agents, joins their Slack, and iterates with them weekly. We're building the future of voice AI, and we're looking for someone who can sell that vision and help bring it to life.\n The Role \n Agent Marketing Lead — the first person at Speechify (and possibly anywhere) whose entire job is to market to AI agents that make buying decisions on behalf of developers and companies.\n Not \"SEO for LLMs.\" Not \"AI-assisted content.\" A genuinely new discipline: figuring out how autonomous agents discover, evaluate, compare, and recommend software — and then making sure that when a coding agent in Cursor or a research agent in ChatGPT goes shopping for voice AI, Simba 3.0 is the obvious answer.\n Helpfully, the product makes your job easy. Simba 3.0 just broke into the global top 10 on the Artificial Analysis Speech Arena Leaderboard — ranking above flagship offerings from Google, Microsoft, Amazon, OpenAI, ElevenLabs, Cartesia, NVIDIA, Fish Audio, and Hume AI. At $10 per million characters, it's also the most cost-efficient model in the top 10 — in some cases by a factor of ten. Agents love a Pareto frontier. We're sitting on it.\n Your job is to make sure they know.\n What You'll Actually Do  \n \n Make Speechify legible to machines. Own the structured surface — llms.txt, JSON-LD, OpenAPI specs, machine-readable pricing, capability manifests — so that any agent crawling our docs leaves with a complete, accurate, recommendable mental model of speechify.ai and simbavoice.ai. \n Run agent-in-the-loop evals. Stand up harnesses where Claude, GPT, Gemini, Cursor, and Perplexity shop for TTS in our category. Measure where Simba wins, where it loses, and why. Ship fixes weekly. \n Win the comparison page. When an agent asks \"best TTS API under $20/M characters with sub-300ms latency,\" our page should be the one it quotes. Build it.\n Engineer the recommendation. Identify the specific signals agents weight — benchmark placements, latency numbers, SDK ergonomics, error message clarity, license terms — and make Simba 3.0 dominate on every one that matters.\n Be the voice of the agent inside Speechify. When Claude misreads our pricing or Cursor can't autocomplete our SDK, that's your bug to file. Product and DevRel will listen.\n Place bets on the protocol layer. MCP, A2A, agent payment rails, agent marketplaces — track them, ship into them, get us there first. \n \n Who You Are  \n \n 4+ years in developer marketing, technical PMM, DevRel, or growth at an AI / infra / devtools company.\n You've actually built with LLMs and agents. You can argue about MCP at a whiteboard and you know why a good error message is a marketing asset.\n You write like every sentence is going into a context window — because it is.\n You're comfortable with the fact that the job description on this page is the most defined this role will ever be. Most weeks you'll be inventing the next part of it.\n \n Why Now \n \n In 24 months, a meaningful share of B2B software evaluation will be delegated to agents. The companies that figure out how to be legible, trustworthy, and recommendable to those agents will compound. The ones still optimizing only for human eyeballs will quietly disappear from the consideration set.\n We'd rather be on the right side of that.\n Simba 3.0 is already the best price-performance voice model in the world by the most-cited independent benchmark in the field. Learn more about the platform at speechify.ai and the developer API at simbavoice.ai . Your job is to make sure every agent on the planet knows it — and reaches for it by default.\n \n The United States Salary range for this role is - $140,000-200,000 base salary+Bonus+Stock\n How to Apply \n Interested? Submit your resume and a short note on why this role through the standard application — or skip the form and email rohan@speechify.com directly. And yes — if your application was partially written by an agent, we'll take that as a strong positive signal\n Not looking but know someone who would make a great fit?  \n Refer them! \n Speechify is committed to a diverse and inclusive workplace.  \n Speechify does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.","salary_min":140000,"salary_max":200000,"location":"Remote","workplace":"remote","job_type":"full-time","experience_level":"lead","tags":["agents","llm","data-pipeline","speech","payments"],"apply_url":"https://job-boards.greenhouse.io/speechify/jobs/5999199004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-19T02:43:42Z","expires_at":"2026-06-29T14:07:37.211572Z","created_at":"2026-05-27T14:07:50.699812Z","updated_at":"2026-05-30T14:07:37.322681Z","company_name":"Speechify","company_slug":"speechify","company_logo_url":"https://www.google.com/s2/favicons?domain=speechify.com\u0026sz=128","quality_score":85,"url":"https://aidevboard.com/job/e4947f7b-32bf-402a-911d-5b9dc7fe363e"},{"id":"709e94c2-f57a-495a-98d1-d3e69477a688","company_id":"dc461dc0-20f9-4429-a009-6cb473fc466c","title":"Forward Deployed Engineer, Onboarding ","slug":"forward-deployed-engineer-onboarding-914045ee","description":"Why AssemblyAI \n AssemblyAI builds the best-in-class Voice AI models powering the next generation of voice applications. Our models serve 600M+ inference calls monthly, process 1M+ hours of audio daily, and power 2 billion+ end-user experiences. The Voice AI space is at an inflection point; we’re looking for folks truly excited to join a small team and help define the future of the industry.\n We are one of the most capital-efficient AI companies on the planet - with under 100 people generating roughly $500K ARR per employee, we sit among the top 5 most revenue-dense teams within the fastest-growing AI companies today. That's not an accident; it's a deliberate choice to stay lean, move fast, and give every person on the team outsized ownership and impact. With thousands of customers including Granola, Fireflies, Figure AI, and CallRail, the company has real scale - processing over 2 million hours of audio daily and handling more than 1 million API calls every day. This is a rare growth-stage opportunity where the business is proven and the trajectory is steep, but the team is still small enough that your fingerprints are on everything.\n If you've ever felt buried under layers of bureaucracy, starved of real ownership, or frustrated watching your work disappear into a slow-moving org, AssemblyAI is built differently. The company operates as a true meritocracy, with no heavy planning or approval processes and no gatekeeping on the tools or information you need. For anyone who genuinely cares about voice AI, not as a trend to chase, but as a technology to build,  this is the place where the most interesting problems at the most interesting scale are being solved by a team small enough that you'll actually know everyone's name.\n We’re committed to creating a space where our employees can bring their full selves to work and have equal opportunity to succeed. No matter your race, gender identity or expression, sexual orientation, religion, origin, ability, age, veteran status, if joining this mission speaks to you, we encourage you to apply!\n About the role: \n We’re looking for a Forward Deployed Engineer to drive new-customer activation across our product lines. This person will own the front of our GTM funnel—the moments between a developer’s first API call and their first month of production traffic—proactively reaching out as usage ramps, debugging deep into customer code when a deal stalls on a technical blocker, tuning model configurations to fit each use case, and stewarding leads through the security, paperwork, and architecture decisions it takes to get to consistent production usage.\n This person should have a strong software-engineering background paired with the instincts of a great seller, being equal parts engineer and quota-carrier, and be deeply interested in the developer experience of building with voice AI.\n This is a cross-functional role that requires partnership with our Sales, Support, Product, and Engineering teams. You’ll be willing to wear many hats, passionate about unblocking customers, and excited to operate with high energy across many concurrent leads with expansive ownership.\n What You’ll Do: \n \n Own a portfolio of in-flight leads across AssemblyAI’s product lines, driving them from first API call to consistent production usage against a quarterly new-customer goal.\n Reach out proactively the moment a developer’s usage ramps, diagnosing what’s blocking them from scaling and removing it before they go quiet.\n Read unfamiliar customer codebases quickly and ship working patches alongside their engineers, debugging real-time audio, WebSocket, and latency issues directly in their service.\n Tune model configurations for each use case—end-of-turn detection, keyterms, language coverage, async vs. real-time—and translate the tradeoffs to technical and non-technical stakeholders alike.\n Run discovery and architecture conversations about customer use cases, knowing when to push back and when to lean in.\n Steward technical wins through to commercial close: DPAs, MSAs, security reviews, stakeholder mapping, and multi-threading the buying team.\n Hand off graduated accounts cleanly with the context and written history required for a smooth transition.\n Aggregate lead feedback into structured signals for Product and Engineering, making sure the self-serve experience improves measurably because of the patterns you surface.\n Build internal tooling—agents, dashboard, AI workflows—that help the team operate ahead of the wave as lead volume grows.\n \n What You’ll Need: \n \n 2+ years of relevant experience as a software engineer, forward-deployed engineer, sales engineer, solutions architect, technical account manager, or similar customer-facing engineering role.\n Strong hands-on engineering skills—comfortable reading an unfamiliar Python or JavaScript/Node service, finding the bug, and writing the patch.\n Working fluency with real-time and streaming protocols, including ","salary_min":150000,"salary_max":200000,"location":"New York, NY","workplace":"remote","job_type":"full-time","experience_level":"junior","tags":["speech","llm"],"apply_url":"https://job-boards.greenhouse.io/assemblyai/jobs/4693817005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-08T17:42:27Z","expires_at":"2026-06-29T14:04:37.097461Z","created_at":"2026-05-10T14:05:02.612079Z","updated_at":"2026-05-30T14:04:37.212974Z","company_name":"AssemblyAI","company_slug":"assemblyai","company_logo_url":"https://www.google.com/s2/favicons?domain=assemblyai.com\u0026sz=128","quality_score":85,"url":"https://aidevboard.com/job/709e94c2-f57a-495a-98d1-d3e69477a688"}],"page":1,"per_page":20,"total":188,"total_pages":10}