ML Infrastructure Engineer (AI)

Location: Remote

Salary: $240,000–$290,000 annually; the stated range is for candidates in the U.S.

Company

Runway builds AI world models that combine art and science to simulate the world and advance generative AI, robotics, scientific discovery, and creative applications.

What you will do

• Own the model evaluation platform end to end, including systems for generating, annotating, reviewing, and adapting outputs at frontier scale.
• Design CLIs, APIs, GUIs, and storage layers that make evaluation fast, collaborative, reproducible, and scalable.
• Partner with research teams working on video, image, audio, agents, and robotics to define measurement needs and generalize solutions into the platform.
• Set standards for metric definitions, reproducibility, reporting formats, confidence, and ship/no-ship decisions.
• Support adoption across training, production model serving, and other research workflows.
• Contribute to ML Platform tools and systems used to train and serve frontier models.

Requirements

• 5+ years of experience building ML infrastructure or data platforms in production, including work with evaluation, experimentation, or benchmarking systems.
• Strong Python and PyTorch skills, with hands-on experience running large-batch GPU workloads on Kubernetes.
• Experience designing data pipelines and storage for large volumes of media or model outputs, including versioning and reproducibility.
• Familiarity with experimental statistics, including paired comparisons, confidence intervals, multiple-comparison pitfalls, and inter-rater agreement.
• Ability to build internal tools from the command line to the browser, lead a broad technical area, and drive a roadmap across multiple teams.
• Understanding of the full model development lifecycle, including data, training, evaluation, and serving, together with a pragmatic approach to production reliability.

Nice to have

• Experience building evaluation suites for generative models.
• Hands-on work with LLM- or VLM-as-judge pipelines.
• Experience with online experimentation platforms and connecting offline metrics to product outcomes.
• Prior experience evaluating agents or robotics policies.

Culture & Benefits

• Remote, full-time role within the ML Platform engineering organization.
• Access to advanced models, agents, GPUs, storage, and cloud services.
• Cloud-native infrastructure using multiple clouds, Kubernetes-native tooling, and internal orchestration systems.
• Collaborative environment emphasizing openness, learning, humility, and diversity.
• Competitive salary range with a commitment to pay equity; compensation may vary outside the U.S.