Engineering Manager, Inference Developer Productivity

Responsibilities:- Build and lead a high-performing teamfocused on developer productivity for the Inference organization, hiring engineers who combine infrastructure expertise with a service-oriented mindset- Own accelerator toolchain managementacross GPU (CUDA), TPU, and Trainium platformskeeping compilers, drivers, libraries, and frameworks current, compatible, and well-tested so that Inference engineers can focus on model serving rather than environment issues- Build infrastructure for efficient accelerator usageduring developmentincluding devbox environments, automation for pre- and post-landing validation, and shared tooling that reduces the friction of working across heterogeneous hardware- Establish and drive productivity metricsacross the Inference org, creating dashboards, alerts, and processes that surface slowdowns early (e.g., smoke tests red for extended periods, build times regressing, toolchain breakages) and ensure rapid resolution- Identify and eliminate inefficienciesacross Inference engineering workflowsproactively finding bottlenecks, toil, and friction points that slow down the org, and building systems or driving process changes to address them- Partner with Anthropic's Infrastructure orgto align on company-wide developer productivity initiatives, contribute Inference-specific requirements, and avoid duplicating effort while ensuring Inference's specialized needs (multi-accelerator support, large-scale testing) are well-served- Coach and develop engineerson your team, providing clear direction, actionable feedback, and growth opportunities in a fast-moving environmentYou may be a good fit if you:- 3+ years of engineering management experience, ideally leading infrastructure, platform, or developer productivity teams- Strong technical background in systems engineering, build/test infrastructure, or ML infrastructureyou can go deep on toolchain issues, CI/CD pipelines, and developer workflow optimization- Experience managing toolchains or development environments for compute-intensive workloads (ML training/inference, HPC, large-scale distributed systems)- Familiarity with at least one accelerator ecosystem (CUDA/GPU, TPU, or Trainium/AWS Neuron) and an appetite to learn the others- A track record of defining and using engineering metrics to drive organizational improvementyou've built dashboards, set SLOs on developer workflows, or led initiatives to measurably improve engineering velocity- Experience partnering across organizational boundariesyou know how to advocate for your team's needs while contributing to shared infrastructure efforts- Strong communication skills and the ability to influence without authority across a technical organization- A genuine passion for making other engineers more productive, and the empathy to understand their pain points deeplyStrong candidates may also:- Experience with ML compiler toolchains (XLA, Triton, NeuronX) or accelerator driver/firmware management at scale- Background in building or running shared development environments (devboxes, remote development, ephemeral environments) for hardware-dependent workflows- Experience with CI/CD systems at scale, particularly for workloads involving accelerator hardware- Familiarity with Kubernetes-based development and job scheduling environments- Prior experience in a developer productivity or platform engineering role at a fast-growing AI/ML companyThe annual compensation range for this role is listed below.For sales roles, the range provided is the roles On Target Earnings (

Created: 2026-02-09