{"access":{"advertiser_pricing_url":"https://aidevboard.com/pricing","catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"cb6610fe-3f66-4e07-b765-55c7b851bcf3","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Staff ML Engineer, Generative Model Performance \u0026 Efficiency","slug":"staff-ml-engineer-generative-model-performance-efficiency-d4d7a93b","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The Simulator Team at Waymo builds state-of-the-art simulations of realistic environments for testing, training, and validation of the Waymo Driver. Our team is a diverse, and collaborative group of machine learning (ML) engineers, software engineers, and ML research engineers. We develop industry-leading simulation solutions using advanced generative and reconstructive ML algorithms, to model the real world, encompassing realistic agents, roads, traffic systems, weather, and the full sensor suite (Camera, Lidar, Radar).\n To accelerate the fidelity, scalability, controllability, and richness of our simulations, we are pushing the frontiers of 3D world modeling. We leverage state-of-the-art ML technologies trained on large-scale datasets to create dynamic and semantically rich virtual worlds, directly impacting the development and validation of the Waymo Driver.\n In this role, you will report to a Senior Staff Engineering Manager\n  \n You will: \n \n Analyze model architectures and identify bottlenecks in training and inference performance (e.g., memory bandwidth, compute, communication).\n Apply and develop techniques such as quantization (e.g., FP8, INT4), pruning, knowledge distillation, and efficient attention mechanisms.\n Optimize model code for specific hardware accelerators (TPUs, GPUs), leveraging compiler features and low-level libraries (e.g., XLA).\n Experiment with different model partitioning and sharding strategies (e.g., data, tensor, pipeline parallelism, expert parallelism) to improve scalability and efficiency.\n Design and implement low-latency, high-throughput serving solutions for generative models and optimize training pipelines to reduce training time.\n Build and maintain tools for performance analysis, profiling (e.g., xprof), and debugging of ML models.\n \n  \n You have: \n \n MS or PhD in Computer Science, Machine Learning, Robotics, or a related field.\n 5+ years of experience with deep learning architectures (especially Transformers, Diffusion Models, MoEs), algorithms, and optimization techniques.\n Proficiency in JAX, Flax, and potentially TensorFlow/PyTorch.\n Expertise in using profiling tools (e.g., XProf, Perfetto, NVIDIA Nsight) to diagnose performance issues in ML workloads.\n Hands-on experience with quantization, pruning, distillation, and other model compression methods.\n Strong programming skills in Python and potentially C++, with experience in software development best practices.\n \n  \n We prefer: \n \n Knowledge of TPU and GPU architectures and how to optimize code for them.\n Familiarity with ML compilers like XLA and an understanding of how they translate high-level code to efficient hardware instructions.\n Understanding of concepts related to training and serving models across multiple devices and machines.\n Experience contributing to frameworks and libraries that improve training speed and scalability (e.g., JAX, Gemax, XManager).\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary","salary_min":251000,"salary_max":310000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["robotics","search","pytorch","autonomous-vehicles","deep-learning","diffusion-models","tensorflow","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8027424","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-29T22:08:47Z","expires_at":"2026-08-14T14:06:31.47041Z","created_at":"2026-06-30T14:04:23.960313Z","updated_at":"2026-07-15T14:06:31.694588Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/cb6610fe-3f66-4e07-b765-55c7b851bcf3"},{"id":"adcfc258-e967-46de-8d48-76960b1055f0","company_id":"cfda17be-0e63-4ab8-81e8-2132d849cd01","title":"Member of Technical Staff - Research Engineer","slug":"member-of-technical-staff-research-engineer-0876ca83","description":"About Black Forest Labs \n We're the team behind Latent Diffusion, Stable Diffusion, and FLUX—foundational technologies that changed how the world creates images and video. We’re creating the generative models that power how people make images and video—tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started.\n Headquartered in Freiburg, Germany with a growing presence in San Francisco, we’re scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity.\n Why This Role \n Large-scale training is where research ideas become real, and where many of the hardest problems are no longer cleanly separated into “research” or “engineering.” A promising architecture only matters if we can train it stably, efficiently, and correctly across large GPU fleets.\n In this role, you will be embedded in production training and help where the hardest systems and performance problems arise: attention performance, custom kernels, low-precision training, profiling, memory behavior, data movement, distributed training stability, and throughput regressions. You will work directly with researchers, but your output will often be code, measurements, kernels, debugging tools, and training-system changes that make better research possible.\n We are open to a range of seniority for this role. The common thread is deep technical ownership: you should be able to make progress in ambiguous training-system problems, verify your results, and own the outcome.\n What You’ll Work On \n \n Improve the performance, reliability, and numerical stability of production training runs for large multimodal generative models\n Profile full training steps across model code, attention, kernels, data loading, encoders, communication, optimizer steps, checkpointing, and memory pressure\n Implement and validate GPU-level optimizations: fused kernels, attention paths, low-precision matmuls, quantization kernels, CUDA/Triton/CuTe/CUTLASS experiments, and no-compile alternatives where they make sense\n Push lower-precision training forward, including FP8 / MXFP8 / FP4-style paths, weight and activation quantization, accumulation choices, convergence risk, and quality tradeoffs against baseline training runs\n Work with researchers to translate architecture changes into efficient training implementations, and help distinguish real model-quality progress from changes that only look good in a microbenchmark\n Debug distributed training failures: NaNs, loss spikes, silent numerical drift, memory leaks, stragglers, bad nodes, NCCL issues, and throughput cliffs\n Build benchmarking and profiling harnesses that make performance claims trustworthy across hardware, shapes, sequence lengths, and training configurations\n Help the training team move quickly when an urgent bottleneck appears, while turning repeated failures into better abstractions and tools\n \n What We’re Looking For \n \n Experience working deeply on large-scale training systems, ideally as part of a training group working closely with researchers\n Strong PyTorch fluency, including comfort reading and modifying low-level training code rather than only using high-level APIs\n Experience with distributed training concepts such as FSDP, tensor/model/context/sequence parallelism, activation checkpointing, NCCL, and overlapping compute and communication\n Hands-on experience improving training throughput, memory footprint, or stability in real training runs\n Experience profiling GPU workloads with tools like Nsight Systems, Nsight Compute, torch profiler, trace viewers, or custom telemetry\n Practical GPU performance judgment: you may use modern coding agents and tools as much as you want, but you need the understanding to verify correctness, numerical behavior, and performance, and to own the result\n Understanding of low-precision training and quantization tradeoffs: FP8, MXFP8, FP4/NVFP4-style formats, scaling, accumulation, numerical validation, and convergence risk\n Good research judgment: you can partner with researchers on ablations, understand what the measurements do and do not prove, and keep optimization work tied to model-quality outcomes\n Comfortable operating in ambiguity: sometimes the task is a clean implementation, sometimes it is a production fire, and sometimes it is figuring out which of three plausible explanations is actually true\n \n We'd be especially excited if you: \n \n Have supported or co-owned training for a frontier foundation model that shipped or reached a major release\n Have written or substantially improved forward/backward GPU kernels, or have shown you can make progress on kernel-level work with strong measurement and validation discipline\n Have worked on attention performance, variable sequence length training, non-standard attention patterns\n Have experience on Hopper or Blackwell-class","salary_min":180000,"salary_max":290000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["diffusion-models","generative-ai","llm","search","pytorch","distributed-systems","gpu","research"],"apply_url":"https://job-boards.greenhouse.io/blackforestlabs/jobs/5286037008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-29T19:48:39Z","expires_at":"2026-08-14T14:15:26.407504Z","created_at":"2026-06-30T14:12:56.164349Z","updated_at":"2026-07-15T14:15:26.507914Z","company_name":"Black Forest Labs","company_slug":"black-forest-labs","company_logo_url":"https://www.google.com/s2/favicons?domain=blackforestlabs.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/adcfc258-e967-46de-8d48-76960b1055f0"},{"id":"a8d08f57-0c6f-40d4-b42c-4fa9061feaed","company_id":"2114efab-ea67-411b-bfb8-7899153105f3","title":"Member of Technical Staff, Inference ","slug":"member-of-technical-staff-inference-86b5008f","description":"Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.\n\n\n\n\nABOUT THE ROLE\n\nWe're looking for an inference runtime engineer to push the boundaries of what's possible in LLM and diffusion model serving. Models grow larger. Architectures shift: mixture-of-experts, multimodal, agentic. Every breakthrough demands innovations on the inference engine itself. You'll work at the core of vLLM, optimizing how models execute across diverse hardware and architectures. Your work will directly impact how the world runs AI inference.\n\n\n\n\nSKILLS AND QUALIFICATIONS\n\nMinimum qualifications:\n\n - Bachelor's degree or equivalent experience in computer science, engineering, or similar.\n\n - Deep understanding of transformer architectures and their variants.\n\n - Strong programming skills in Python with experience in PyTorch internals.\n\n - Experience with LLM inference systems (vLLM, TensorRT-LLM, SGLang, TGI).\n\n - Ability to read and implement model architectures and inference techniques from research papers.\n\n - Demonstrate the ability to contribute performant and maintainable code and debug in complex ML codebases.\n\nPreferred qualifications:\n\n - Deep understanding of KV-cache memory management, prefix caching, and hybrid model serving.\n\n - Familiarity with RL frameworks and algorithms for LLMs.\n\n - Experience with multimodal inference (audio/image/video/text).\n\n - Contributions to open-source ML or system infrastructure projects.\n\nBonus points if you have:\n\n - Implemented core features in vLLM or other inference engine projects.\n\n - Contributed to vLLM integrations (verl, OpenRLHF, Unsloth, LlamaFactory, etc).\n\n - Written widely-shared technical blogs or side projects on vLLM or LLM inference.\n   \n   \n\n\nLOGISTICS\n\n - Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.\n\n - Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.\n\n - Visa sponsorship: We sponsor visas on a case-by-case basis.\n\n - Benefits: Inferact offers generous health, dental, and vision benefits as well as 401(k) company match.","salary_min":200000,"salary_max":400000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","reinforcement-learning","mlops","llm","diffusion-models","agents","research","inference"],"apply_url":"https://jobs.ashbyhq.com/inferact/43c0ca54-fcf5-41fa-83a1-38800c75ccc0/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-18T18:55:18.703Z","expires_at":"2026-08-14T14:13:18.851627Z","created_at":"2026-06-28T14:10:47.134304Z","updated_at":"2026-07-15T14:13:18.959602Z","company_name":"Inferact","company_slug":"inferact","company_logo_url":"https://www.google.com/s2/favicons?domain=inferact.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a8d08f57-0c6f-40d4-b42c-4fa9061feaed"},{"id":"edd81527-9709-4b01-8b95-c78ef07b4bc1","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Senior Machine Learning Engineer, Simulation Evaluation","slug":"senior-machine-learning-engineer-simulation-evaluation-2b2339ea","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The Challenge \n Waymo’s simulator is one of the most complex virtual environments ever built. It blends deterministic logic, physical dynamics, and state-of-the-art Generative AI to create a training ground for the Waymo Driver. The Simulator Evaluation team faces the ultimate data challenge: How do you mathematically prove that a virtual world is \"real\" ?\n We are seeking visionary machine learning engineers and researchers to architect the scalable deep learning systems, novel data workflows, and eval tools that power our research roadmap. In this role, you will pioneer the machine learning and generative vision paradigms required to define and measure the realism of our multimodal world models. Your work will define the state of the art for autonomous simulation, directly steering our research trajectory and the capabilities of the Waymo Driver.\n You will: \n \n Lead the design, development and deployment of cutting-edge evaluation approaches to assess realism of state-of-the-art multimodel world models and generative systems for simulation use cases at Waymo.\n Architect and implement robust and scalable machine learning pipelines for tuning, evaluating, and deploying large-scale discriminator models for the purposes of simulator realism evaluation. \n Evaluate open-source and production-ready video generation techniques that measure realism (e.g. temporal stability, multi-modal consistency, geometric discrepancy, condition following, etc.)\n Apply vision language models to evaluate semantic understanding and controllability across our world simulation products.\n Collaborate with research teams across Waymo and Alphabet to integrate advancements in 4D world modeling and generative AI into production systems.\n Mentor engineers on the team and provide technical guidance on architecture and execution.\n \n You have: \n \n Bachelor's, Master's, or PhD in computer science, machine learning, robotics, or a related field.\n Five or more years of experience in machine learning engineering or applied deep learning, supported by a portfolio of shipped products or peer-reviewed publications.\n Proficient programming skills in Python and hands-on experience with modern machine learning frameworks such as Jax, Flax, or PyTorch.\n Experience designing and implementing evaluation frameworks for complex systems or machine learning models.\n \n We prefer: \n \n Track record of training large-scale generative models (diffusion models, flow matching, vision language models, etc.)\n A PhD and demonstrated success delivering machine learning products focused on 3D generative models, world models, or video generation.\n Experience simulating sensor data, including camera, lidar, and radar, or modeling semantic scenes.\n Experience developing autonomous systems, robotics software, or autonomous vehicle simulations.\n Experience training and optimizing large-scale models on GPU or TPU clusters for efficient production serving.\n Professional experience writing C++ for high-performance production systems.\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $213,000 — $263,000 USD","salary_min":213000,"salary_max":263000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","autonomous-vehicles","diffusion-models","deep-learning","generative-ai","robotics","evaluation","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8001797","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-06-13T17:49:43Z","expires_at":"2026-08-14T14:06:26.933168Z","created_at":"2026-06-28T14:04:26.926315Z","updated_at":"2026-07-15T14:06:27.056408Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/edd81527-9709-4b01-8b95-c78ef07b4bc1"},{"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, Voice 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, emotional, financial and physical health. We are focused on building a positive, safe, inclusive and inspiring place to work. Our benefits include: \n \n Robust medical, dental and vision options with employer contributions for HSA, FSA and DFSA\n 401k matching program \n Flexible Time Off, 10 paid holidays, 5 days sick leave\n Parental, Medical and Family care leave \n Generous cell-phone, wellness and office set up stipends \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","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["pytorch","agents","generative-ai","search","speech","gpu","diffusion-models","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":"2026-08-14T14:06:38.162814Z","created_at":"2026-05-14T14:05:27.875309Z","updated_at":"2026-07-15T14:06:38.310779Z","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":"805ed157-8bdf-4b5b-91cb-a8a94d8c0226","company_id":"a0000000-0000-0000-0000-000000000003","title":"Technical Lead Manager, Physical AI","slug":"technical-lead-manager-physical-ai-13b9c024","description":"Scale AI is the data engine for the entire AI industry. Our mission is to accelerate the development of AI applications by providing organizations with the high-quality data they need. The Physical AI team at Scale is focused on the next frontier: building general AI that can reason and act in the physical world. By leveraging Scale’s massive data infrastructure, we are helping frontier labs build Foundation Models for Physical AI that will redefine the future of automation.\n Role Overview \n As the Technical Lead Manager (TLM) for the Physical AI team of Scale , you will bridge the gap between cutting-edge Machine Learning research and physical robot deployment. You will lead a high-performing team of Research Engineers while remaining a hands-on technical contributor (~60% of your time).\n Your primary focus will be the development and evaluation of Large-Scale Foundation Models (e.g VLAs, World models) that allow robots and AVs to generalize across diverse tasks, environments, and morphologies.\n Key Responsibilities \n Technical Leadership \u0026 Research \n \n Model Scaling: Direct research into scaling laws for Physical AI, determining how to best utilize massive datasets for pre-training and fine-tuning generalist policies.\n VLA and World model development: Develop novel methods for developing and evaluating models, including new Physical AI industry benchmarks\n Hands-on Modeling: Actively write code to implement, train and test SOTA architectures. Conduct research on Physical AI data collection, cross-embodiment training, and policy fine-tuning.  \n Data Strategy: Collaborate with internal labeling teams to design \"robotic-native\" data pipelines, including the use of VLMs for automated trajectory annotation and data synthesis.\n Collaborate closely with customers to drive the industry forward in using Scale data \n \n Team Management \u0026 Execution \n \n Mentorship: Lead and grow a team of 4-6 elite Physical AI  researchers, fostering a culture of high-velocity experimentation and rigorous evaluation.\n Paper-to-Product: Translate the latest research from NeurIPS, ICRA, and CVPR into production-ready features for Scale’s Physical AI partners.\n Cross-functional Alignment: Work with cross-functional teams (e.g Product and Operations) to bring our research breakthroughs into production. \n \n Required Qualifications \n AI/ML Excellence \n \n Deep Learning Mastery: Expert-level proficiency in PyTorch , with deep knowledge of Transformer architectures , Attention mechanisms , and Self-Supervised Learning .\n VLM/VLA Experience: Proven track record of working with Vision-Language Models (e.g., CLIP, PaLM-E) and adapting them for spatial reasoning or embodied tasks.\n Generative AI: Experience with Diffusion Models for sequence generation or Generative World Models for predictive modeling.\n \n Physical AI \u0026 Software Background \n \n Embodied AI: Strong understanding of Physical AI stack, including imitation learning, reinforcement learning (RL), and multi-modal sensor fusion.\n Infrastructure: Experience with large-scale distributed training across GPU clusters and high-performance data loading.\n Leadership: 1+ years of experience leading technical teams or projects in a research-intensive environment.\n \n Nice to Haves: \n \n Publication Record: First-author publications at top-tier AI/ML conferences (NeurIPS, CVPR, ICRA, CoRL).\n Hardware Generalization: Experience building models that work across different robot types (arms, mobile bases, humanoids).\n Sim-to-Real: Experience with high-fidelity simulators (e.g., Isaac Gym, MuJoCo) and the nuances of physical domain adaptation.\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. \n The base salary range for this full-time position in the location of San Francisco is:\n $248,800 — $311,000 USD \n PLEASE NOTE:  Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. \n About Us: \n ","salary_min":248800,"salary_max":311000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","pre-training","cloud","gpu","generative-ai","robotics","diffusion-models","search"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4693453005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-08T16:16:36Z","expires_at":"2026-08-14T14:01:52.273472Z","created_at":"2026-05-10T14:01:23.558094Z","updated_at":"2026-07-15T14:01:52.398636Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/805ed157-8bdf-4b5b-91cb-a8a94d8c0226"},{"id":"5def081b-793e-405f-844f-117748963cdc","company_id":"4bafa9b3-1ed9-4f7e-9920-f618b1ac0b15","title":"Staff Software Engineer (AI)","slug":"staff-ai-engineer-80afc06b","description":"The Team + The Role\n Our Emerging Team is focused on building AI Products for our product experience (PX) platform. We build from the ground up to explore, prototype, and ship AI-native experiences that change how software teams understand and serve their users. This is not an AI layer added to existing product; it is a deliberate bet on what product intelligence looks like next. The team operates with high autonomy, moves quickly, and builds products without clear precedents.\n As a Staff Software Engineer (AI), you will sit at the intersection of deep technical capability and strong product judgment. You will design and build production-grade AI systems, including RAG pipelines, agentic workflows, and LLM-powered features, while making clear tradeoffs across prompting, fine-tuning, architecture, evaluation, and deployment. You will also partner closely with product, design, and engineering stakeholders to frame the right problems and communicate technical decisions clearly.\n This role is based in our New York office.\n What this looks like day-to-day\n \n Applied AI systems: Design and build AI-native systems, including RAG pipelines, agentic workflows, and LLM-powered product features. You will take ideas from prototype through production and ensure they can support real users.\n Model strategy: Make principled decisions about when to prompt, when to fine-tune, and when to use a different technical approach entirely. You will explain those tradeoffs clearly to engineers and non-engineers.\n Evaluation and guardrails: Instrument and evaluate model outputs rigorously by defining evaluation frameworks and identifying hallucinations early. You will implement guardrails that hold up under real-world usage and load.\n Productionize AI ownership: Own model deployment, monitoring, latency optimization, cost management, and reliability at scale. You will ensure AI systems are observable, performant, and production-ready.\n Full-stack delivery: Contribute across the stack when needed to get complete AI products in front of users. This team ships products, not just models, and you will help close the gap between technical capability and user experience.\n Product partnership: Partner closely with product and design to frame problems well before implementation begins. You will push back when the framing is wrong and help the team stay focused on what is worth building.\n Technical leadership: Stay current on the research and tooling landscape, including transformers, diffusion architectures, orchestration frameworks, and emerging agent patterns. You will bring relevant advances back to the team and help raise the technical bar.\n \n Who You Are\n Beyond the qualifications, we hire through a specific lens. These aren't buzzwords; they're the things we'll actually look for in how you talk about your work.\n You're a builder, not a maintainer.\n You're most energized when there isn't a clear path yet, and you get to define it. You don't wait for direction; you identify gaps, shape solutions, and drive them forward. At Pendo, great Staff AI Software Engineers don't just follow instructions; they operate as strategic advisors, influencing decisions, guiding stakeholders, and elevating how we work.\n You're AI-curious - genuinely.\n You're not using AI tools occasionally. You're rewiring how you work around them. You're faster, sharper, and more prolific because of it, and you bring that energy to everything — how you approach your work, how you prep, how you communicate, how you think. We want someone who sees AI as a multiplier, not a shortcut.\n Must-haves\n \n Deep hands-on experience building and shipping LLM-powered systems, including retrieval-augmented generation, tool use, and agent orchestration frameworks.\n Demonstrated ability to set technical direction for AI systems across teams, establish architectural patterns, make foundational model strategy decisions, and raise the bar for AI engineering quality.\n Experience owning outcomes across team boundaries, including identifying capability gaps, driving alignment across engineering and product, and influencing how a broader organization approaches AI.\n Strong command of model evaluation, including designing evaluation suites, reasoning about overfitting and bias-variance tradeoffs, and systematically detecting and mitigating hallucinations.\n Solid understanding of modern model architectures, including transformers and diffusion models, with the ability to make informed decisions about when and how to apply them.\n Production Productionize AI experience, including model deployment, monitoring pipelines, and latency, cost, and reliability optimization in live environments.\n Strong full-stack fundamentals and comfort working across backend and frontend systems to ship complete, user-facing AI products.\n Exceptional communication skills, with the ability to explain complex technical decisions clearly to engineers, product managers, and executives.\n Demonstrated product judgment and the","salary_min":245000,"salary_max":275000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["agents","generative-ai","rag","mlops","llm","diffusion-models","alignment","data-pipeline"],"apply_url":"https://job-boards.greenhouse.io/pendo/jobs/8533499002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-05T18:04:00Z","expires_at":"2026-08-14T14:21:29.390232Z","created_at":"2026-05-06T14:25:29.842467Z","updated_at":"2026-07-15T14:21:29.483942Z","company_name":"Pendo","company_slug":"pendo","company_logo_url":"https://www.google.com/s2/favicons?domain=pendo.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/5def081b-793e-405f-844f-117748963cdc"},{"id":"45002517-2a02-492f-89c0-a43d9b4b0ed6","company_id":"4bafa9b3-1ed9-4f7e-9920-f618b1ac0b15","title":"Sr. Software Engineer (AI)","slug":"sr-ai-engineer-594a2514","description":"The Team + The Role\n Our Emerging Team is focused on building AI Products for our product experience (PX) platform. We build from the ground up to explore, prototype, and ship AI-native experiences that change how software teams understand and serve their users. This is not an AI layer added to existing product; it is a deliberate bet on what product intelligence looks like next. The team operates with high autonomy, moves quickly, and builds products without clear precedents.\n As a Sr. Software Engineer (AI), you will sit at the intersection of deep technical capability and strong product judgment. You will design and ship applied AI systems, including RAG pipelines, agentic workflows, and LLM-powered features, from prototype through production. You will make principled technical decisions, evaluate model behavior rigorously, and communicate tradeoffs clearly to engineers and non-engineers alike.\n This role is based in our New York office.\n What this looks like day-to-day\n \n Applied AI systems: Design and build AI systems including RAG pipelines, agentic workflows, and LLM-powered features. You will take work from prototype through production and ensure it can hold up in real customer environments.\n Technical decision-making: Make principled decisions on when to prompt, when to fine-tune, and when to use a different tool entirely. You will explain these tradeoffs clearly so the team can move quickly without sacrificing quality.\n Model evaluation: Instrument and evaluate model outputs rigorously by defining evaluation frameworks and catching hallucinations early. You will implement guardrails that can withstand real-world load and production use.\n Productionize AI ownership: Own model deployment, monitoring, latency optimization, cost management, and reliability at scale. You will help ensure AI systems are observable, efficient, and dependable in production.\n Full-stack product shipping: Contribute across the stack when needed because this team ships products, not just models. You will work across backend and frontend to get AI-powered experiences in front of users.\n Product partnership: Partner closely with product and design to frame problems well before writing code. You will push back when the framing is wrong and help the team focus on what should be built, not just what can be built.\n Research and tooling awareness: Stay current on the research and tooling landscape, including transformers, diffusion architectures, orchestration frameworks, and emerging agent patterns. You will bring relevant advances back to the team and apply them thoughtfully.\n \n Who You Are\n Beyond the qualifications, we hire through a specific lens. These aren't buzzwords; they're the things we'll actually look for in how you talk about your work.\n You're a builder, not a maintainer.\n You're most energized when there isn't a clear path yet, and you get to define it. You don't wait for direction; you identify gaps, shape solutions, and drive them forward. At Pendo, great Sr. AI Software Engineers don't just follow instructions; they operate as strategic advisors, influencing decisions, guiding stakeholders, and elevating how we work.\n You're AI-curious - genuinely.\n You're not using AI tools occasionally. You're rewiring how you work around them. You're faster, sharper, and more prolific because of it, and you bring that energy to everything — how you approach your work, how you prep, how you communicate, how you think. We want someone who sees AI as a multiplier, not a shortcut.\n Must-haves\n \n Deep hands-on experience building and shipping LLM-powered systems, including retrieval-augmented generation, tool use, and agent orchestration frameworks.\n Strong technical depth in system design, including choosing the right architecture, identifying failure modes early, and making tradeoffs that hold up across the product lifecycle.\n Experience owning technical quality beyond your own features, including setting standards, catching problems in review, and improving shared infrastructure and tooling.\n Strong command of model evaluation, including designing evaluation suites, reasoning about overfitting and bias-variance tradeoffs, and systematically detecting and mitigating hallucinations.\n Solid understanding of modern model architectures, including transformers and diffusion models, with the judgment to decide when and how to apply them.\n Production Productionize AI experience, including model deployment, monitoring pipelines, and latency, cost, and reliability optimization in a live environment.\n Strong full-stack fundamentals with the ability to work across backend and frontend systems to ship complete, user-facing AI products.\n Exceptional communication skills with the ability to explain complex technical decisions clearly to engineers, product managers, and executives.\n Demonstrated product thinking, including the ability to ask whether something should be built before deciding how to build it.\n \n Nice-to-haves\n \n Experience fine","salary_min":209000,"salary_max":235000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["rag","alignment","diffusion-models","llm","agents","generative-ai","data-pipeline","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/pendo/jobs/8533495002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-05T18:03:52Z","expires_at":"2026-08-14T14:21:29.310439Z","created_at":"2026-05-06T14:25:29.754022Z","updated_at":"2026-07-15T14:21:29.412198Z","company_name":"Pendo","company_slug":"pendo","company_logo_url":"https://www.google.com/s2/favicons?domain=pendo.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/45002517-2a02-492f-89c0-a43d9b4b0ed6"},{"id":"60e8de00-2b5a-4ee6-bdb3-1037d5268168","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Senior, Machine Learning Engineer - End-to-End","slug":"senior-machine-learning-engineer-end-to-end-1a34ca94","description":"About the Company  \n At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.\n A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight. \n Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer. \n Meet the Team: As a Senior Machine Learning Engineer – End-to-End (E2E), you will develop and scale learning-based systems that connect multi-modal perception inputs to driving behavior, enabling safe, efficient, and human-like autonomy for real-world freight operations. You’ll work at the intersection of perception, prediction, and planning, contributing to unified learning pipelines that operate in closed-loop environments. This role focuses on owning meaningful portions of the E2E stack, improving model performance at scale, and driving iteration through data, experimentation, and cross-functional collaboration. This is a hands-on engineering role focused on execution, iteration, and delivery. What You’ll Do \n \n Own development and delivery of End-to-End ML models that map multi-modal sensor inputs (camera, LiDAR, radar, maps) to driving-relevant outputs (trajectories, cost functions, or intermediate representations)\n Train and evaluate models using large-scale datasets from fleet logs, simulation, and synthetic data\n Analyze model performance, identify failure modes, and drive data-driven improvements in robustness and generalization\n Design and refine training pipelines, data workflows, and evaluation strategies to improve iteration speed and model quality\n Contribute to model architecture decisions, including approaches such as imitation learning, reinforcement learning, transformers, and vision-language-action (VLA) models\n Collaborate closely with Perception, Prediction, Planning, and Simulation teams to ensure alignment across the autonomy stack\n Support integration of E2E models into simulation and on-vehicle systems for closed-loop validation\n Improve tooling, experimentation workflows, and reproducibility across the team\n Mentor junior engineers and contribute to team-level best practices and technical discussions\n \n What You’ll Need to Succeed \n \n Bachelor’s degree with 6+ years, Master’s with 4+ years, or PhD with 0–2 years of experience in Machine Learning, Robotics, Computer Science, or a related field with a track record of publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, CoRL)\n Experience developing and deploying ML models for autonomous systems, robotics, or complex decision-making environments\n Strong programming skills in Python and PyTorch, with ability to write production-quality ML code\n Experience training and evaluating models using large-scale datasets and distributed compute environments\n Solid understanding of ML architectures used in E2E systems, such as Transformers, BEV models, VLA/VLM approaches, or diffusion models\n Proven ability to debug model behavior, analyze performance metrics, and drive iterative improvements\n Experience contributing to or influencing model architecture and training strategies\n Ability to work cross-functionally and integrate ML systems into larger autonomy pipelines\n \n Bonus Points \n \n Experience developing End-to-End or mid-to-end models for autonomous driving or robotics\n Experience with vision-language models (VLMs) or vision-language-action (VLA) systems\n Familiarity with closed-loop simulation and evaluation frameworks\n Experience with reinforcement learning or imitation learning in real-world systems\n Experience with distributed training frameworks (e.g., Ray)\n Understanding of vehicle dynamics, motion planning, or multi-agent systems\n \n Work Location: For this position, we are open to hiring in Ann Arbor, MI (U.S.) office work locations in a hybrid capacity. We are also open to hiring Remote in the United States.\n Perks of Being a Full-time Torc’r  \n Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:   \n \n A competitive compensation package that includes a bonus component and stock options\n 100% paid medical, dental, and vision premiums for full-time employees   \n 401K plan with a 6% employer matchFlexibility in schedule and generous paid vacation (available immediately after start date)Company-wide holiday office closures\n AD+D and Life Insurance  \n \n At Torc, we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’rs and do not discriminate based on race, religion,","salary_min":226400,"salary_max":271700,"location":"Ann Arbor, MI","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["robotics","reinforcement-learning","agents","payments","pytorch","diffusion-models","distributed-systems","autonomous-vehicles"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8518797002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-22T17:31:11Z","expires_at":"2026-08-14T14:07:35.114574Z","created_at":"2026-04-30T05:49:30.050228Z","updated_at":"2026-07-15T14:07:35.252432Z","company_name":"Torc Robotics","company_slug":"torc-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=torc.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/60e8de00-2b5a-4ee6-bdb3-1037d5268168"},{"id":"031d9dd7-d656-4e6f-b47e-afaa9fbc6057","company_id":"75dcf7c0-5121-45f1-8d1b-6bfbfe15072f","title":"Helix AI Engineer, Generative AI","slug":"helix-ai-engineer-generative-ai-397c6aa2","description":"Figure is an AI robotics company developing autonomous general-purpose humanoid robots. Our goal is to build embodied AI systems that can perceive, reason, and act in the real world. Figure is headquartered in San Jose, CA, and this role requires 5 days/week in-office collaboration.\n Our Helix team is responsible for developing the core AI systems that power humanoid autonomy. We are looking for a Helix AI Engineer, Generative AI to build and scale generative models that enable robots to understand, simulate, and interact with the physical world. This role focuses on training and deploying diffusion and generative models across vision, video, and multimodal domains, with applications spanning perception, data generation, and model-based reasoning.\n Responsibilities \n \n Design, train, and deploy large-scale generative models, with a focus on diffusion-based approaches for vision, video, and multimodal data\n Develop models that improve robot perception, world modeling, and prediction from raw sensory inputs\n Build generative systems for synthetic data creation, augmentation, and dataset scaling for robot learning\n Explore and implement state-of-the-art techniques in diffusion, generative modeling, and multimodal foundation models\n Optimize training pipelines for large-scale generative models across distributed systems\n Work closely with data, training infrastructure, and agent teams to integrate generative models into the full autonomy stack\n Evaluate model quality, robustness, and generalization across real-world scenarios\n Contribute to the design of scalable experimentation frameworks for generative model development\n \n Requirements \n \n Experience training and deploying generative models (diffusion, autoregressive, or related approaches) at scale\n Strong understanding of modern deep learning techniques for vision and/or multimodal systems\n Proficiency in Python and deep learning frameworks such as PyTorch\n Experience working with large-scale datasets and distributed training systems\n Strong experimental rigor and ability to iterate quickly on model performance\n Solid software engineering skills and ability to build reliable, maintainable systems\n Ability to operate independently and own ambiguous, high-impact technical problems\n \n Bonus Qualifications \n \n Experience with diffusion models for image or video generation\n Experience with multimodal foundation models (vision-language or vision-language-action)\n Background in synthetic data generation or simulation for robotics or embodied AI\n Experience optimizing large-scale training (multi-node, GPU clusters, etc.)\n Familiarity with 3D, video prediction, or world models\n Prior work in robotics, embodied AI, or real-world ML systems\n Publication record in machine learning, computer vision, or generative modeling\n \n The US  base  salary range for this full-time position is between $200,000 - $400,000\n The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.","salary_min":200000,"salary_max":400000,"location":"San Jose, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["gpu","diffusion-models","generative-ai","pytorch","computer-vision","distributed-systems","robotics","deep-learning"],"apply_url":"https://job-boards.greenhouse.io/figureai/jobs/4671699006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-09T15:08:07Z","expires_at":"2026-08-14T14:07:52.438469Z","created_at":"2026-04-13T09:42:03.139006Z","updated_at":"2026-07-15T14:07:52.594698Z","company_name":"Figure AI","company_slug":"figure-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=figure.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/031d9dd7-d656-4e6f-b47e-afaa9fbc6057"},{"id":"d8108d17-7273-4ca4-ac36-9747b09deeac","company_id":"e597788a-bd36-460e-8d1a-40fdbfbcc5c3","title":"World Model Research Scientist- Physical AI","slug":"world-model-research-scientist-physical-ai-e1bfa299","description":"Kodiak Robotics, Inc. was founded in 2018 and has become a leader in autonomous ground transportation committed to a safer and more efficient future for all. The company has developed an artificial intelligence (AI) powered technology stack purpose-built for commercial trucking and the public sector. The company delivers freight daily for its customers across the southern United States using its autonomous technology. In 2024, Kodiak became the first known company to publicly announce delivering a driverless semi-truck to a customer. Kodiak is also leveraging its commercial self-driving software to develop, test and deploy autonomous capabilities for the U.S. Department of Defense.\n Kodiak is building AI that doesn't just perceive the world, it learns how the physics of the world works. We are developing large-scale generative world models that learn to predict realistic, physically consistent futures from real-world sensor data. This capability serves as the foundation for scalable closed-loop training, validation, and long-tail scenario generation, and is distilled into the onboard models that drive our autonomous trucks. We are looking for a research scientist to lead the design and development of world models capable of generating multi-sensor, multi-view, temporally coherent driving scenarios conditioned on actions, 3D scene context, and text. \n  \n In this role, you will: \n \n Design and train generative world models that synthesize realistic multi-camera video and LiDAR conditioned on ego trajectories, 3D scene context, and text \n Research and implement conditional diffusion architectures for driving, including spatiotemporal attention, latent space design, and action-conditioned generation \n Develop techniques for multi-view geometric consistency in generated outputs, drawing on neural rendering, cross-view attention, and 3D-aware generative approaches \n Build methods for joint multimodal generation that maintain cross-sensor consistency between camera, LiDAR, and radar outputs \n Design evaluation frameworks that measure world model quality beyond pixel-level metrics, including scenario fidelity and autoregressive stability \n Scale training pipelines to learn from thousands of hours of real-world driving data across multiple sensor modalities \n \n What you'll bring: \n \n PhD in Computer Science, AI, Robotics, or a related field, with a focus on generative modeling, neural rendering, or video synthesis \n Strong publication record or demonstrated research contributions in diffusion models, video generation, neural radiance fields, 3D-aware generative models, or world models \n Experience with neural rendering and view synthesis and an understanding of multi-view geometric consistency \n Proficiency working with multimodal sensor data (camera, LiDAR, radar) and familiarity with 3D representations such as BEV grids, voxel fields, or tri-planes \n Strong implementation skills in Python and PyTorch, with experience training large generative models at scale using distributed training \n Passion for building AI that understands and predicts the physical world to enable safe autonomous driving \n \n What We Offer: \n \n Competitive compensation package including equity and annual bonuses \n Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Cigna, and  MetLife (including a medical plan with infertility benefits) \n MetLife Legal Services, Identity \u0026 Fraud Protection, Hospital Indemnity Insurance, Accident Insurance, \u0026 Critical Illness Insurance \n Flexible PTO, 10 paid holidays, and generous parental leave policies \n Our office is centrally located in Mountain View, CA \n Office perks: dog-friendly, free catered lunch, a fully stocked kitchen, and free EV charging \n Long Term Disability, Short Term Disability, Life Insurance \n Wellbeing Benefits - Headspace through Cigna, Calm through Kaiser, One Medical, Gympass, Spring Health through Cigna, Rula (mental health navigation)  \n Fidelity 401(k) \n Commuter, FSA, Dependent Care FSA, HSA \n Various incentive programs (referral bonuses, patent bonuses, etc.) \n The pay range listed below reflects the base salary  in our SF/Silicon Valley location,  across several internal levels. Actual starting pay will be based on job-related factors including: work location, experience, relevant training, education, skill level and performance during interview. Total compensation at Kodiak includes base pay, equity, bonus and a competitive benefits package\n California Pay Range\n $190,000 — $250,000 USD \n  \n At Kodiak, we strive to build a diverse community working towards our common company goals in a safe and collaborative environment where harassment of any kind is strictly prohibited. Kodiak is committed to equal opportunity employment regardless of race, ethnicity, religion, gender identity, sexual orientation, age, disability, or veteran status, or any other basis protected by applicable law.\n  \n In alignment with its business operations, Kodiak adheres to all r","salary_min":190000,"salary_max":250000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["robotics","computer-graphics","diffusion-models","data-pipeline","distributed-systems","autonomous-vehicles","pytorch","research"],"apply_url":"https://job-boards.greenhouse.io/kodiak/jobs/4203253009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-03-27T19:53:56Z","expires_at":"2026-08-14T14:10:22.900106Z","created_at":"2026-04-13T09:41:40.245143Z","updated_at":"2026-07-15T14:10:23.024809Z","company_name":"Kodiak","company_slug":"kodiak","company_logo_url":"https://www.google.com/s2/favicons?domain=kodiak.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d8108d17-7273-4ca4-ac36-9747b09deeac"},{"id":"aa4a4bb5-14e2-4f37-b9e2-312763c67570","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Senior, ML Engineer - Neural Rendering","slug":"senior-ml-engineer-neural-rendering-3df05be6","description":"About the Company      \n At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.     A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight.     Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.     \n Meet The Team:       \n Torc is marching towards its AV 3.0 strategy. Simulation and Data Generation are key elements towards autonomous trucking. The team builds the sensor simulation based on Neural Rendering and Generative models for Torc’s data-driven simulator. High-quality data from novel scenarios close the domain data gap, solving one of the biggest challenges for safe autonomous driving.  The team works on state-of-the-art approaches for Neural Rendering and data generation for camera, LiDAR, and Radar sensor data, that contributes to the Torc’s data workflow for training and validation across the complete AV stack.     \n What You’ll Do:       \n \n Implement the latest research advances in Neural Rendering and generative models  \n Translate cutting edge solution in the domain of autonomous driving for high-quality Camera, LiDAR and Radar sensor simulations \n Support implementing a neural rendering framework that allows to scale perception simulation and AV 3.0 training \n Integrate the framework in a cloud environment and automate the pipeline to allow scaling for the target verification and validation of our autonomous trucks   \n Own development projects in the team – From research, design, to implementation, testing and deployment \n Design, implement, test and deploy shippable production quality software starting from early prototypes using disciplined software development processes.\n Work in the cloud machine learning ecosystem alongside other machine learning services existing in the company.\n Proactively assess current capabilities to identify areas for improvement proposing solutions that align with core strategy and operation. \n Demonstrate project management skills, serving as project lead guiding less experienced team members in multiple facets of project execution, coaching and mentoring as needed.     \n \n What You’ll Need to Succeed:       \n \n Proficiency in Python and deep learning frameworks such as PyTorch.  \n PhD or equivalent work experience of 6+ years in relevant fields (CS, Robotics, Electrical Engineering) with industry experience in shipping production software. \n Proven expertise in Neural Rendering (Neural Radiance Fields and 3D Gaussian Splatting) and generative models (Diffusion Models, Flow Matching). \n Background in Computer Graphics, 3D Reconstruction, or 3D Computer Vision. \n Considered highly skilled and proficient in discipline; conducts complex, important work under minimal supervision and with wide latitude for independent judgment.    \n Experience with VDI and cloud based machine learning development environments.\n Expected to drive alignment across team interfaces to the rest of the organization.  \n Designs, maintains and owns team technical solutions and drives consensus.   \n Mentors and guides engineers within the group.     \n \n Bonus Points!     \n \n Experience with autonomous driving or robotics perception in production environments.  \n Proficiency with CUDA programming for efficient rendering of large-scale scenes \n Publications in top-tier CV/AI/Graphics conferences (CVPR/ECCV/ICCV, NeurIPS/ICLR/ICML, SIGGRAPH) or journals \n Experience with MLOps and infrastructure tools (Ray) \n Familiarity with 3D labeling, calibration, and sensor simulation pipelines.   \n Handling of Autonomous Driving Sensors  –  Multiple timestamp, multiple sensor data from cameras, lidars, and radars    \n \n Perks of Being a Full-time Torc’r      Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:       \n \n A competitive compensation package that includes a bonus component and stock options  \n 100% paid medical, dental, and vision premiums for full-time employees   \n 401K plan with a 6% employer match   \n Flexibility in schedule and generous paid vacation (available immediately after start date) \n AD+D and Life Insurance     \n \n At Torc, we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’rs and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, ","salary_min":160800,"salary_max":212300,"location":"Montreal, Canada","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["autonomous-vehicles","robotics","payments","mlops","pytorch","computer-vision","diffusion-models","deep-learning"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8429809002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-02-19T16:47:38Z","expires_at":"2026-08-14T14:07:35.721667Z","created_at":"2026-04-13T09:41:38.491922Z","updated_at":"2026-07-15T14:07:35.856544Z","company_name":"Torc Robotics","company_slug":"torc-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=torc.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/aa4a4bb5-14e2-4f37-b9e2-312763c67570"},{"id":"3faaf4a4-8132-4a05-8a0b-d01bbee31909","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Staff Software Engineer, Simulator Evaluation","slug":"staff-software-engineer-simulator-evaluation-e5cb31d3","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n Waymo’s simulator is one of the most complex virtual environments ever built. It blends deterministic logic, physical dynamics, and state-of-the-art Generative AI to create a training ground for the Waymo Driver. The Simulator Evaluation team faces the ultimate data challenge:  How do you mathematically prove that a virtual world is \"real\"? \n We are looking for a Staff Software Engineer to act as the Technical Architect for this domain.  You will work at the intersection of software engineering and AI, ensuring that our simulated worlds—whether driven by explicit rules or foundation models—provide a trustworthy representation of reality.\n In this Staff-level role, you will report to a Senior Staff Software Engineering Manager and act as a Technical Lead, bridging the gap between deep technical metrics and high-level product strategy.\n You will: \n \n Architect the Eval Rubric: You will define the \"Definition of Done\" for simulation realism. You will look ahead at product goals (e.g., launching in snow, highway driving) and architect the evaluation roadmap that ensures our simulation fidelity matures in lockstep with onboard needs.\n The \"Critic\" for the System: You will design the comprehensive mathematical frameworks that validate our hybrid world. You will decide how we balance distinct evaluation needs—from verifying logical rules and dynamics to measuring the distribution quality of generative AI models.\n Build at Scale: You will lead the design of large-scale, extensible evaluation platforms (C++/Python). You ensure our metric pipelines are not just scripts, but robust distributed systems capable of providing clear, reproducible signals on petabytes of data.\n Strategic, Cross-functional Leadership You will act as the technical bridge between organizations. You will partner closely with AI research and other simulation teams, as the eval workflows you build will drive rapid innovation and research roadmaps.\n \n You have: \n \n System-Level Engineering:\n \n 8+ years of industry experience, with a focus on building complex data systems, evaluation platforms, or back-end infrastructure.\n Expertise in designing systems that scale (C++, Python, distributed computing), with a strong focus on API design and maintainability.\n \n Advanced Quantitative Intuition:\n \n You don't just calculate metrics; you design frameworks. You can debate the merits of different statistical approaches (e.g., determining the right confidence intervals for safety-critical validation) and apply them to complex, non-deterministic systems.\n You have experience designing and implementing evaluation frameworks for complex systems or machine learning models.\n \n Product-Aware Leadership:\n \n Experience creating technical strategies that span multiple teams. You can translate high-level product requirements into concrete engineering problems (e.g., \"To launch in snow, we need X specific friction metrics by Q2\").\n \n \n We prefer: \n \n Background in fields that blend code, math, and simulation: Autonomous Vehicles, Algorithmic Trading, AdTech/Search Ranking, Machine Learning, or Robotics.\n Familiarity with the validation of Generative AI (LLMs, Diffusion models) and/or classical simulation systems (Agent-based modeling, heuristics).\n Experience driving technical roadmaps for large-scale systems or validation frameworks.\n Experience guiding a team or system through a major architectural shift.\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $238,000 — $302,000 USD","salary_min":238000,"salary_max":302000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["generative-ai","llm","diffusion-models","distributed-systems","search","api-design","robotics","autonomous-vehicles"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7602321","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-02-10T18:57:32Z","expires_at":"2026-08-14T14:06:32.318717Z","created_at":"2026-04-13T09:40:22.329102Z","updated_at":"2026-07-15T14:06:32.446435Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3faaf4a4-8132-4a05-8a0b-d01bbee31909"},{"id":"4ebf0edc-d373-4fbc-83b5-776051ed8f4b","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Senior, ML Engineer - Neural Rendering","slug":"senior-ml-engineer-neural-rendering-1f571eb5","description":"About the Company      \n At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.     A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight.     Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.     \n Meet The Team:       \n Torc is marching towards its AV 3.0 strategy. Simulation and Data Generation are key elements towards autonomous trucking. The team builds the sensor simulation based on Neural Rendering and Generative models for Torc’s data-driven simulator. High-quality data from novel scenarios close the domain data gap, solving one of the biggest challenges for safe autonomous driving.  The team works on state-of-the-art approaches for Neural Rendering and data generation for camera, LiDAR, and Radar sensor data, that contributes to the Torc’s data workflow for training and validation across the complete AV stack.     \n What You’ll Do:       \n \n Implement the latest research advances in Neural Rendering and generative models  \n Translate cutting edge solution in the domain of autonomous driving for high-quality Camera, LiDAR and Radar sensor simulations \n Support implementing a neural rendering framework that allows to scale perception simulation and AV 3.0 training \n Integrate the framework in a cloud environment and automate the pipeline to allow scaling for the target verification and validation of our autonomous trucks   \n Own development projects in the team – From research, design, to implementation, testing and deployment \n Design, implement, test and deploy shippable production quality software starting from early prototypes using disciplined software development processes.\n Work in the cloud machine learning ecosystem alongside other machine learning services existing in the company.\n Proactively assess current capabilities to identify areas for improvement proposing solutions that align with core strategy and operation. \n Demonstrate project management skills, serving as project lead guiding less experienced team members in multiple facets of project execution, coaching and mentoring as needed.     \n \n What You’ll Need to Succeed:       \n \n Proficiency in Python and deep learning frameworks such as PyTorch.  \n PhD or equivalent work experience of 6+ years in relevant fields (CS, Robotics, Electrical Engineering) with industry experience in shipping production software. \n Proven expertise in Neural Rendering (Neural Radiance Fields and 3D Gaussian Splatting) and generative models (Diffusion Models, Flow Matching). \n Background in Computer Graphics, 3D Reconstruction, or 3D Computer Vision. \n Considered highly skilled and proficient in discipline; conducts complex, important work under minimal supervision and with wide latitude for independent judgment.    \n Experience with VDI and cloud based machine learning development environments.\n Expected to drive alignment across team interfaces to the rest of the organization.  \n Designs, maintains and owns team technical solutions and drives consensus.   \n Mentors and guides engineers within the group.     \n \n Bonus Points!     \n \n Experience with autonomous driving or robotics perception in production environments.  \n Proficiency with CUDA programming for efficient rendering of large-scale scenes \n Publications in top-tier CV/AI/Graphics conferences (CVPR/ECCV/ICCV, NeurIPS/ICLR/ICML, SIGGRAPH) or journals \n Experience with MLOps and infrastructure tools (Ray) \n Familiarity with 3D labeling, calibration, and sensor simulation pipelines.   \n Handling of Autonomous Driving Sensors  –  Multiple timestamp, multiple sensor data from cameras, lidars, and radars    \n \n Perks of Being a Full-time Torc’r      Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:       \n \n A competitive compensation package that includes a bonus component and stock options  \n 100% paid medical, dental, and vision premiums for full-time employees   \n 401K plan with a 6% employer match   \n Flexibility in schedule and generous paid vacation (available immediately after start date) \n AD+D and Life Insurance     \n \n At Torc, we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’rs and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, ","salary_min":177300,"salary_max":234000,"location":"Ann Arbor, MI","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["deep-learning","computer-vision","mlops","gpu","diffusion-models","autonomous-vehicles","computer-graphics","pytorch"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8375567002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-01-15T21:22:54Z","expires_at":"2026-08-14T14:07:35.613926Z","created_at":"2026-04-13T09:41:38.412303Z","updated_at":"2026-07-15T14:07:35.774521Z","company_name":"Torc Robotics","company_slug":"torc-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=torc.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4ebf0edc-d373-4fbc-83b5-776051ed8f4b"},{"id":"b4f58af6-d4eb-4b45-aeb2-2a6048a6311a","company_id":"ccb23d77-c69e-462b-a941-02ce99527e78","title":"Machine Learning Engineering TL, Behavior Planning","slug":"machine-learning-engineering-tl-behavior-planning-f85ea7bf","description":"Who we are \n Aurora’s mission is to deliver the benefits of self-driving technology safely, quickly, and broadly.\n The Aurora Driver  will create a new era in mobility and logistics, one that will bring a safer, more efficient, and more accessible future to everyone.\n  \n At Aurora, you will tackle massively complex problems alongside other passionate, intelligent individuals, growing as an expert while expanding your knowledge. For the latest news from Aurora, visit  aurora.tech  or follow us on  LinkedIn .\n  \n Aurora hires talented people with diverse backgrounds who are ready to help build a transportation ecosystem that will make our roads safer, get crucial goods where they need to go, and make mobility more efficient and accessible for all. We are seeking a ML Engineering TL to join the Behavior Planning Team . This is a high-impact role where you will sit at the frontier of autonomous vehicle technology. You won't just be applying AI and ML methods; you will be advancing the state-of-the-art for how a self-driving system reasons about the world, interacts with humans on the road, and masters complex, high-dimensional decision-making.\n In this role, you will \n \n Define the architecture of our onboard planning models: Develop and deploy large-scale models trained with Imitation Learning and Reinforcement Learning that enable the Aurora Driver to navigate complex environments with human-like fluidity and superhuman safety.\n Build our next-generation simulation engine: Architect cutting-edge offboard foundation models that power our simulation engine, creating realistic \"world models\" to test the Aurora Driver against an infinite variety of edge cases.\n Revolutionize evaluation: Develop powerful offboard critic models that can evaluate driving behavior at scale, identifying subtle nuances in comfort, progress, and safety that traditional heuristics miss.\n Bridge research and production: Reach new frontiers of autonomous driving technology by pushing forward the state-of-the-art, but also deploy your models on real production vehicles that drive on public roads and must meet the highest standards of safety. \n Mentor and lead: Serve as a technical lead, guiding junior engineers and shaping the long-term roadmap for ML-based planning at Aurora, including the onboard and off-board ecosystem that is needed to support it.\n \n Required Qualifications \n \n MS or PhD in Robotics, Machine Learning, Computer Science, or a related quantitative field, or equivalent practical experience.\n 8 + years of experience developing state-of-the-art ML models, either in a research or production setting.\n Hands-on experience working on Imitation Learning or Reinforcement Learning applied to physical or simulated agents.\n Experience training large models on massive datasets using distributed computing.\n Fluency in Python, with a focus on writing high-performance, maintainable code.\n Deep experience with PyTorch (preferred) or another modern ML framework, and a mastery of modern ML architectures including Transformers and Diffusion Models.\n \n Desirable Qualifications \n \n A track record of publications in top-tier ML conferences (NeurIPS, ICML, CoRL, CVPR, AAAI).\n Experience deploying complex ML systems in production environments.\n Experience in developing generative models or neural simulators for synthetic data generation.\n Experience leading small or large teams to execute highly technical projects.\n \n The base salary range for this position is $171K - $247K per year. Aurora’s pay ranges are determined by role, level, and location. Within the range, the successful candidate’s starting base pay will be determined based on factors including job-related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future. The successful candidate will also be eligible for an annual bonus, equity compensation, and benefits. \n #LI-JL261 \n #Mid-Senior  \n Working at Aurora At Aurora, we bring together extraordinarily talented and experienced people united by the strength of our values. We operate with integrity, set outrageous goals, and build a culture where we win together — all without any jerks.\n We believe in-person work increases collaboration, empathy and our ability to lead effectively. As a result, we operate in a hybrid work environment where Aurorans are in office at least 3 days per week.\n Our Careers page provides insight into what it is like to work at Aurora, and you can find all the latest updates in our Newsroom .\n Our commitment to safety \n At the core of everything we do is our commitment to safety. Building best-in-class self-driving technology will take time, and we believe that each employee at Aurora has a role in contributing to safety, every step of the way. Aurora expects commitment to our safety policies from every employee, and seeks candidates who take an active responsibility, can contribute to building an atmosphere of trust, an","salary_min":171000,"salary_max":247000,"location":"Mountain View, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["autonomous-vehicles","generative-ai","distributed-systems","diffusion-models","pytorch","reinforcement-learning","robotics","machine-learning"],"apply_url":"https://aurora.tech/jobs/8360046002?gh_jid=8360046002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-01-06T01:24:10Z","expires_at":"2026-08-14T14:06:40.375274Z","created_at":"2026-04-13T09:40:35.258133Z","updated_at":"2026-07-15T14:06:40.505529Z","company_name":"Aurora Innovation","company_slug":"aurora-innovation","company_logo_url":"https://www.google.com/s2/favicons?domain=aurora.tech\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b4f58af6-d4eb-4b45-aeb2-2a6048a6311a"},{"id":"63340998-ed75-4784-97eb-36db3cd706b6","company_id":"ccb23d77-c69e-462b-a941-02ce99527e78","title":"Machine Learning Engineering TL, Behavior Planning","slug":"machine-learning-engineering-tl-behavior-planning-2352b6a8","description":"Who we are \n Aurora’s mission is to deliver the benefits of self-driving technology safely, quickly, and broadly.\n The Aurora Driver  will create a new era in mobility and logistics, one that will bring a safer, more efficient, and more accessible future to everyone.\n  \n At Aurora, you will tackle massively complex problems alongside other passionate, intelligent individuals, growing as an expert while expanding your knowledge. For the latest news from Aurora, visit  aurora.tech  or follow us on  LinkedIn .\n  \n Aurora hires talented people with diverse backgrounds who are ready to help build a transportation ecosystem that will make our roads safer, get crucial goods where they need to go, and make mobility more efficient and accessible for all. We are seeking a ML Engineering TL to join the Behavior Planning Team . This is a high-impact role where you will sit at the frontier of autonomous vehicle technology. You won't just be applying AI and ML methods; you will be advancing the state-of-the-art for how a self-driving system reasons about the world, interacts with humans on the road, and masters complex, high-dimensional decision-making.\n In this role, you will \n \n Define the architecture of our onboard planning models: Develop and deploy large-scale models trained with Imitation Learning and Reinforcement Learning that enable the Aurora Driver to navigate complex environments with human-like fluidity and superhuman safety.\n Build our next-generation simulation engine: Architect cutting-edge offboard foundation models that power our simulation engine, creating realistic \"world models\" to test the Aurora Driver against an infinite variety of edge cases.\n Revolutionize evaluation: Develop powerful offboard critic models that can evaluate driving behavior at scale, identifying subtle nuances in comfort, progress, and safety that traditional heuristics miss.\n Bridge research and production: Reach new frontiers of autonomous driving technology by pushing forward the state-of-the-art, but also deploy your models on real production vehicles that drive on public roads and must meet the highest standards of safety. \n Mentor and lead: Serve as a technical lead, guiding junior engineers and shaping the long-term roadmap for ML-based planning at Aurora, including the onboard and off-board ecosystem that is needed to support it.\n \n Required Qualifications \n \n MS or PhD in Robotics, Machine Learning, Computer Science, or a related quantitative field, or equivalent practical experience.\n 8 + years of experience developing state-of-the-art ML models, either in a research or production setting.\n Hands-on experience working on Imitation Learning or Reinforcement Learning applied to physical or simulated agents.\n Experience training large models on massive datasets using distributed computing.\n Fluency in Python, with a focus on writing high-performance, maintainable code.\n Deep experience with PyTorch (preferred) or another modern ML framework, and a mastery of modern ML architectures including Transformers and Diffusion Models.\n \n Desirable Qualifications \n \n A track record of publications in top-tier ML conferences (NeurIPS, ICML, CoRL, CVPR, AAAI).\n Experience deploying complex ML systems in production environments.\n Experience in developing generative models or neural simulators for synthetic data generation.\n Experience leading small or large teams to execute highly technical projects.\n \n The base salary range for this position is $171K - $247K per year. Aurora’s pay ranges are determined by role, level, and location. Within the range, the successful candidate’s starting base pay will be determined based on factors including job-related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future. The successful candidate will also be eligible for an annual bonus, equity compensation, and benefits. \n #LI-JL261 \n #Mid-Senior  \n Working at Aurora At Aurora, we bring together extraordinarily talented and experienced people united by the strength of our values. We operate with integrity, set outrageous goals, and build a culture where we win together — all without any jerks.\n We believe in-person work increases collaboration, empathy and our ability to lead effectively. As a result, we operate in a hybrid work environment where Aurorans are in office at least 3 days per week.\n Our Careers page provides insight into what it is like to work at Aurora, and you can find all the latest updates in our Newsroom .\n Our commitment to safety \n At the core of everything we do is our commitment to safety. Building best-in-class self-driving technology will take time, and we believe that each employee at Aurora has a role in contributing to safety, every step of the way. Aurora expects commitment to our safety policies from every employee, and seeks candidates who take an active responsibility, can contribute to building an atmosphere of trust, an","salary_min":171000,"salary_max":247000,"location":"Pittsburgh, PA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","pytorch","distributed-systems","robotics","autonomous-vehicles","diffusion-models","generative-ai","machine-learning"],"apply_url":"https://aurora.tech/jobs/8301849002?gh_jid=8301849002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-01-06T01:23:24Z","expires_at":"2026-08-14T14:06:40.454062Z","created_at":"2026-04-13T09:40:35.344908Z","updated_at":"2026-07-15T14:06:40.60007Z","company_name":"Aurora Innovation","company_slug":"aurora-innovation","company_logo_url":"https://www.google.com/s2/favicons?domain=aurora.tech\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/63340998-ed75-4784-97eb-36db3cd706b6"},{"id":"291dc842-7b2c-438e-9f96-a106ec660496","company_id":"a832a7c6-808b-473c-a141-b8f5f7a27286","title":"Research Scientist (Generative Modeling)","slug":"research-scientist-generative-modeling-4073dfee","description":"About World Labs: \n We build foundational world models that can perceive, generate, reason, and interact with the 3D world — unlocking AI's full potential through spatial intelligence by transforming seeing into doing, perceiving into reasoning, and imagining into creating. \n We believe spatial intelligence will unlock new forms of storytelling, creativity, design, simulation, and immersive experiences across both virtual and physical worlds.\n We bring together a world-class team, united by a shared curiosity, passion, and deep backgrounds in technology — from AI research to systems engineering to product design — creating a tight feedback loop between our cutting-edge research and products that empower our users.\n Role Overview \n We are looking for a talented Research Scientist with a strong background in generative modeling, particularly diffusion models, to join our modeling team. This role is ideal for candidates with deep expertise in diffusion models applied to images, videos, or 3D assets and scenes. \n While not required , experience in one or more of the following areas is a strong plus :\n \n Large-scale model training \n Data curation for pretraining or post-training \n Tokenizers and VAEs for image, video, or 3D data \n Long-context architectures \n 3D vision \n \n  \n You will collaborate closely with researchers, engineers, and product teams to bring advanced 3D modeling and machine learning techniques into real-world applications, ensuring that our technology remains at the forefront of visual innovation. This role involves significant hands-on research and engineering work, driving projects from conceptualization through to production deployment. \n What You Will Do:   \n \n Design, implement, and train large-scale diffusion models for generating 3D worlds\n Develop and experiment with large-scale diffusion models to add novel control signals, adapt to target aesthetic preferences, or distill for efficient inference\n Collaborate closely with research and product teams to understand and translate product requirements into effective technical roadmaps.\n Contribute hands-on to all stages of model development including data curation, experimentation, evaluation, and deployment.\n Continuously explore and integrate cutting-edge research in diffusion and generative AI more broadly\n Act as a key technical resource within the team, mentoring colleagues, and driving best practices in generative modeling and ML engineering\n \n  \n Key Qualifications: \n \n 3+ years of experience in generative modeling or applied ML roles, ideally at a startup or other fast-paced research environment\n Extensive experience with machine learning frameworks such as PyTorch or TensorFlow, especially in the context of diffusion models and other generative models\n Deep expertise in at least one area of generative modeling: pre-training, post-training, diffusion distillation, fine-tuning with new conditioning signals, etc for diffusion models\n Strong history of publications or open-source contributions involving large-scale diffusion models\n Strong coding proficiency in Python and experience with GPU-accelerated computing.\n Ability to engage effectively with researchers and cross-functional teams, clearly translating complex technical ideas into actionable tasks and outcomes.\n Comfortable operating within a dynamic startup environment with high levels of ambiguity, ownership, and innovation. \n \n Preferred Qualifications: \n \n Contributions to open-source projects in the fields of computer vision, graphics, or ML.\n Familiarity with large-scale training infrastructure (e.g., multi-node GPU clusters, distributed training environments).\n Experience integrating machine learning models into production environments.\n Led or been involved with the development or training of large-scale, state-of-the-art generative models \n \n Who You Are: \n \n Fearless Innovator: We need people who thrive on challenges and aren't afraid to tackle the impossible.\n Resilient Builder: Impacting Large World Models isn't a sprint; it's a marathon with hurdles. We're looking for builders who can weather the storms of groundbreaking research and come out stronger.\n Mission-Driven Mindset: Everything we do is in service of creating the best spatially intelligent AI systems, and using them to empower people.\n Collaborative Spirit: We're building something bigger than any one person. We need team players who can harness the power of collective intelligence.\n \n  \n We're hiring the brightest minds from around the globe to bring diverse perspectives to our cutting-edge work. If you're ready to work on technology that will reshape how machines perceive and interact with the world - then World Labs is your launchpad.\n  \n Join us, and let's make history together.\n \n Equal Opportunity \u0026 Pay Transparency \n Equal Employment Opportunity \n World Labs is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender iden","salary_min":250000,"salary_max":325000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","tensorflow","generative-ai","computer-vision","diffusion-models","pytorch","gpu","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/worldlabs/jobs/4089324009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-12-29T17:57:42Z","expires_at":"2026-08-14T14:06:49.05507Z","created_at":"2026-04-13T09:40:45.453392Z","updated_at":"2026-07-15T14:06:49.185857Z","company_name":"World Labs","company_slug":"world-labs","company_logo_url":"https://www.google.com/s2/favicons?domain=worldlabs.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/291dc842-7b2c-438e-9f96-a106ec660496"},{"id":"78f3d18e-8c9d-4331-8dd0-bb84e810b4d1","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Senior Machine Learning Engineer, Driver Understanding and Evaluation","slug":"seniorstaff-machine-learning-engineer-6e824641","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The Driver Understanding and Evaluation (DUE) team at Waymo is developing rich metrics for understanding the behavior of the Waymo Driver in the real world, and technologies such as  context and scene analysis to understand driving, understanding and augmenting real world driving data to generate rare driving events, build large scale data infrastructure, improve components such as agents and a realistic simulator. These technologies come together to drive the overall technical strategy and methodology used to evaluate the behavior of the Waymo Driver. \n The DUE Machine Learning team will build and operate scalable machine learning and data systems, simulation workflow and insight tools,  improve and speed up the evaluation and onboard developer journeys. It will combine expert human judgements and advanced machine learning models to deliver training and evaluation data for hundreds of metrics and components that make up the Waymo driver. We are looking for researchers and software engineers who are passionate about developing machine learning techniques for the Evaluation systems on our autonomous vehicles, and have an incessant drive to improve the performance of our technology stack. \n You will: \n \n Lead the design, development and deployment of cutting-edge 4D world models and generative systems for ultra-realistic and controllable sensor and semantics generation for simulation use cases at waymo.\n Architect and implement scalable and robust ML pipelines for training, evaluating, and deploying large-scale generative models into our simulation infrastructure, including techniques like model distillation and quantization.\n Build and scale production-ready video generation techniques (e.g., Diffusion, Flow Matching) to create dynamic and interactive simulation environments.\n Apply Vision Language Models (VLMs) to enhance the semantic understanding and controllability of our world simulation products.\n Partner with world class research teams across Waymo and Alphabet to leverage State-of-The-Art research in 4D world modeling and generative AI into robust, production-ready solutions.\n Mentor and provide technical guidance to other engineers on the team.\n \n You have: \n \n MS or PhD in Computer Science, Machine Learning, Robotics, or a related field.\n 5+ years of experience in ML engineering and applied Deep Learning, with a strong portfolio of shipped products or publication record.\n Proven experience in developing and training large-scale generative models for video generation (e.g., Diffusion models, Flow Matching) or Vision Language Models (VLMs) and their applications.\n Deep expertise in 3D World Modeling or 3D computer vision.\n Familiarity with 3D reconstruction and rendering techniques (e.g., 3D Gaussian Splatting).\n Strong programming skills in Python and experience with ML frameworks such as Jax/Flax, PyTorch or Tensorflow.\n \n We prefer: \n \n PhD and a strong track record of delivering impactful ML products in 3D generative models, world models, or video generation..\n Experience in simulating sensor data (Camera, Lidar, Radar) and/or semantic scenes.\n Experience with autonomous systems, robotics, or autonomous vehicle simulation.\n Experience in training and optimizing large scale models on GPU/TPU clusters for efficient serving.\n Experience in C++ for production systems.\n \n  \n #LI-Hybrid\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. 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Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n Rigorous performance evaluation of the Waymo Driver is a critical part of scaling our ride hailing service and achieving Waymo’s audacious goals. Waymo data scientists work hand-in-hand with engineering teams at each stage of the software development cycle, employing statistical models and developing metrics and measurement frameworks to ensure that the Waymo Driver meets our strict standards for safety, compliance, and driving and service quality. Autonomous driving presents a new paradigm in data science: in addition to leveraging data collected on-road, we generate our own data using state-of-the-art simulation technology—resulting in denser signals and challenging new problems in estimation and experimental design.\n In this hybrid role you will report to a data science manager.\n You will: \n \n Develop evaluation frameworks for autonomous vehicle performance, for large-scale ML models, and for the quality of simulation.\n Develop new metrics, interpret trends, and investigate anomalies in data from simulation and on-road driving.\n Develop novel statistical methods to handle unique aspects of AV data; e.g. rate estimation with rare events, combining real and synthetic data, etc.\n Frame and solve ambiguous problems by scoping technical priorities and innovating on statistical methods.\n Derive data-driven conclusions and communicate findings to senior stakeholders.\n Establish yourself as the point-of-contact for a significant project area by using data to drive technical decisions and demonstrate success.\n Collaborate with Product and Engineering partners developing the Waymo Driver and Waymo’s simulation software; facilitate deployment readiness decisions for both products.\n Mentor other data scientists and provide constructive technical feedback within the team and across Waymo.\n \n You have: \n \n Degree in a quantitative field (e.g. Statistics, Mathematics, Physics)\n 5+ years of industry experience solving data science problems, or a PhD in a quantitative field and 3+ years of industry experience\n Expertise using advanced statistical methods in an applied setting; familiarity with ML systems/models\n Demonstrated knowledge of Python/SQL/R data analysis libraries and packages\n \n We prefer: \n \n PhD in a quantitative field\n A demonstrated track record of independently driving data science projects to deliver business value\n Experience solving problems related to Autonomous Driving or Ride Hailing\n Experience in adjacent relevant areas like Advanced Machine Learning (Deep Learning and Diffusion models), Traffic Modeling, Safety Evaluation or Prediction\n \n   \n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $204,000 — $259,000 USD","salary_min":204000,"salary_max":259000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["deep-learning","diffusion-models","autonomous-vehicles","data-science"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7456042","is_featured":false,"is_sticky":false,"status":"active","published_at":"2025-12-17T22:09:14Z","expires_at":"2026-08-14T14:06:25.848777Z","created_at":"2026-05-27T14:04:35.671538Z","updated_at":"2026-07-15T14:06:25.978759Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8b201db2-31c5-468a-bf56-8929044f813d"},{"id":"a7f6955e-818e-46fe-8e78-fa40ec975427","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Data Scientist","slug":"data-scientist-fd3e14a9","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n Rigorous performance evaluation of the Waymo Driver is a critical part of scaling our ride hailing service and achieving Waymo’s audacious goals. 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