Research Engineer

What You'll Do

• Build and improve the eval and optimization loop for core agents, turning real production usage into measurable improvements in quality, latency, and reliability.
• Systematically improve agent behavior across prompts, programs, routing logic, constraints, and model adaptations, applying techniques like DSPy and GEPA where useful.
• Fine-tune, adapt, and evaluate specialist models for repeatable, high-volume agent tasks where there is clear production feedback or verifiable quality signals.
• Work across the stack when needed, from traces and eval infrastructure to agent orchestration, product workflows, and customer-facing AI behavior.

Requirements

• Strong technical background in software engineering, machine learning, or applied AI, demonstrated through an advanced degree and/or equivalent experience building production AI systems.
• Strong software engineering fundamentals and good judgment for designing, building, and debugging complex systems.
• Experience building evals for AI systems, including datasets, judges, metrics, offline replay, tracing, or regression testing.
• Practical understanding of modern model adaptation and post-training methods, including LoRA/QLoRA, SFT, distillation, preference optimization, reward modeling, DPO/GRPO, and reinforcement learning from verifiable feedback.
• Ownership mindset: drive projects end-to-end, move quickly from real usage, and care about shipping measurable improvements.
• Enjoy following SOTA research into new models, agent architectures, evals, post-training methods, and optimization techniques.

Nice-to-Haves

• Experience with techniques like DSPy and GEPA.
• Background in improving how agents reason over complex enterprise context (users, apps, devices, tickets, licenses, policies, customer-specific data).