Staff Software Engineer, Code RL

Job summary

ABOUT Ant*** Ant***’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

Benefits

Qualifications

• Deep expertise in Python, including static typing, safe async and concurrency patterns, and writing performant Python code
• A track record of designing intuitive, safe APIs or frameworks that other engineers or teams adopted and built on
• Experience working productively in large, evolving, or research-style codebases that you didn't originally write
• Demonstrated ability to anticipate failure modes — especially silent ones — and prevent them structurally through system design, type safety, and testing
• Strong written and verbal communication
• Experience building infrastructure, tooling, or frameworks for machine learning research or RL workflows
• Familiarity with reinforcement learning concepts, agentic systems, or LLM training pipelines
• Experience building or operating large-scale distributed systems
• Experience building client libraries or SDKs on top of sandboxed, containerized, or remote execution platforms
• Experience with large-scale data processing or dataset lifecycle management
• Experience designing plugin systems or extensible class hierarchies used across an organization
• Experience embedding with or consulting for other teams, including successfully handing off systems for others to own

Responsibilities

• You won't own all of this yourself, you'll take on the slices where your depth matters most.
• You'll be a strong fit if you have deep expertise in Python, a refined sense of taste for API and framework design, and hard-won intuition for how complex systems fail — especially silently.
• Design widely-used APIs, frameworks, and abstractions that other engineers and researchers build on, with careful attention to interface legibility and principled defaults
• Embed with research teams on a rotational basis: understand their engineering needs, build systems and APIs that support their work, and transfer ownership so teams can maintain those systems after you rotate off
• Work directly in research codebases, improving reliability and structure without slowing down the research they support
• Anticipate silent failure modes and prevent them structurally through type safety, well-designed invariants, targeted testing, and refactors that shrink the surface area for bugs
• Contribute to the reliability of production RL systems, including monitoring, regression detection, and triage tooling
• Help define engineering standards, review practices, and design patterns for a new team, and mentor researchers and engineers in adopting them
• MINIMUM

Skills

Degrees

Work schedule

Industry