AI Systems Engineer

About Dialpad Dialpad is the AI-native business communications platform. We unify calling, messaging, meetings, and contact center on a single platform - powered by AI that understands every conversation in real time. More than 70,000 companies around the globe, including WeWork, Asana, NASDAQ, AAA Insurance, COMPASS Realty, Uber, Randstad, and Tractor Supply, rely on Dialpad to build stronger customer connections using real-time, AI-driven insights. We’re now leading the shift to Agentic AI: intelligent agents that don’t just analyze conversations but take action by automating workflows, resolving customer issues, and accelerating revenue in real time. Our DAART initiative (Dialpad Agentic AI in Real Time) is redefining what a communications platform can do. Visit dialpad.com to learn more. Being a Dialer At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more. We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves. We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic . Your role We are hiring AI Inference Platform Engineers to build the production systems that serve our in-house AI models at scale. This role sits at the intersection of model development, high-performance runtime systems, and cloud infrastructure. You will help turn trained models and emerging AI capabilities into reliable, observable, low-latency production services running on NVIDIA GPUs in GCP. This is not a research role, and it is not a generic MLOps or support role. It is an implementation-heavy systems engineering role focused on the machinery of inference: model serving, runtime optimization, GPU utilization, deployment safety, traffic management, benchmarking, and production reliability. Our mission is to shorten the path from promising model capability to dependable production impact. We build the shared infrastructure, standards, and release pathways that allow models to move from candidate artifacts into scalable, rollback-safe inference services with clear performance, reliability, and cost characteristics. This is a new team, so the systems and interfaces are still being shaped. You will help define how models are packaged, deployed, benchmarked, monitored, compared, and operated across environments. The work is practical, deeply technical, and closely tied to the company’s broader AI strategy. We are not building one-off demos; we are building the inference platform by which a growing AI organization can repeatedly and safely ship real model-backed products. What you’ll do You will design, build, and improve the systems that connect AI capability development to production inference. Depending on your strengths, your work may include: Inference Serving & Runtime Systems: Build and improve model-serving pathways for low-latency, high-throughput, high-availability inference workloads. GPU Infrastructure & Utilization: Operate and optimize containerized workloads on Kubernetes/GCP, with a focus on efficient use of NVIDIA GPUs, memory, storage, and networking. Model Server Integration: Work with model-serving frameworks and runtimes such as vLLM, Triton, TGI, or similar systems, adapting them to internal deployment, observability, and release requirements. Traffic & Release Safety: Enable shadow serving, canary rollouts, staged deployments, candidate-versus-incumbent comparisons, and fast rollback mechanisms for model-backed services. Benchmarking & Evaluation Infrastructure: Build tooling to measure latency, throughput, cost, saturation behavior, and reliability under realistic production traffic. Artifact Lifecycle: Improve how model and capability artifacts are packaged, versioned, promoted, deployed, and rolled back across environments. Observability & Debuggability: Strengthen runtime telemetry, structured logging, tracing, dashboards, and alerting so engineers can understand model-serving behavior in production. Efficiency & Scale: Contribute to strategies that improve compute efficiency, GPU utilization, autoscaling behavior, and cost-performance tradeoffs across the inference platform. Skills you’ll bring Production Engineering Experience: 6+ years of professional software engineering experience, with a track record of shipping backend services, infrastructure systems, or production platforms that matter. Strong Software Fundamentals: Proficiency in writing maintainable production code in Python, Go, or another backend-oriented language, with strong debugging and systems-thinking skills.