Applied Research - RL & Agents

Skills

About the Role

Design and deploy reinforcement-learning methods, post-training systems, evaluations, and AI agents for real-world workflows. Build agent infrastructure, integrate frameworks, maintain distributed training and inference pipelines, and develop observability systems for reliable production deployments.

Requirements

• Strong machine learning engineering background
• Experience in post-training, reinforcement learning, or large-scale model alignment
• Experience with agent frameworks and tooling such as DSPy, LangGraph, MCP, and Stagehand
• Familiarity with distributed training and inference frameworks such as vLLM, sglang, Accelerate, Ray, and Torch
• Research contributions through publications, open-source contributions, or benchmarks in machine learning or reinforcement learning
• Technical writing abilities
• Research taste
• External collaboration and open-source community engagement

Responsibilities

• Design and iterate on AI agents for workflow automation, reasoning-intensive tasks, and large-scale decision-making
• Develop systems and frameworks for reliable and efficient agent operation
• Translate ambiguous objectives into technical requirements
• Deploy agents, evaluations, and harnesses for real-world tasks
• Shape verifiers, environments, training services, and research platform offerings
• Build reference implementations and recipes
• Design and implement reinforcement learning and post-training methods
• Build evaluations and harnesses for reasoning, robustness, and agentic behavior
• Prototype multi-agent and memory-augmented systems
• Integrate agent frameworks
• Architect and maintain distributed training and inference pipelines
• Develop observability and monitoring systems

Benefits

• Equity incentives
• Flexible work
• Visa sponsorship
• Relocation support
• Professional development budget
• Team off-sites
• Conference attendance