Research Engineer, Post-Training

Responsibilities

• Design and run post‑training workflows that improve the behavior, reliability, and usefulness of AI systems
• Develop datasets, preference signals, evaluation suites, reward models, fine‑tuning workflows, and feedback loops for applied AI use cases
• Investigate how different post‑training techniques affect system behavior across enterprise workflows and production constraints
• Build infrastructure for experimentation, model comparison, regression testing, and behavior analysis
• Partner with AI Researchers to explore new post‑training methods and with AI Engineers to apply successful techniques in deployed systems
• Analyze model outputs, failure modes, human feedback, and production traces to identify opportunities for behavioral improvement
• Create repeatable processes for adapting AI systems to customer domains while preserving robustness, transparency, and maintainability
• Communicate clearly with internal teams and customer stakeholders about model behavior, evaluation results, limitations, and tradeoffs

• Experience Improving Model Behavior:
  You have worked with fine‑tuning, preference optimization, reinforcement learning, reward modeling, synthetic data, evals, or related post‑training techniques
• Strong Programming and Experimentation
  
  Skills:
  
  You can build training and evaluation pipelines, run controlled experiments, analyze results, and iterate quickly
• Research‑Oriented Builder:
  You care about understanding why behavior changes, not just whether a benchmark improves
• AI Systems Mindset:
  You understand that model behavior is shaped by data, prompts, tools, retrieval, evaluators, and deployment context—not model weights alone
• AI‑Native Working Style:
  You use AI tools daily to accelerate coding, analysis, debugging, experimentation, and research exploration
• Bias Toward Measurement:
  You make behavioral improvements concrete through evaluations, comparisons, regression tests, and production‑relevant metrics
• Comfort with Applied Constraints:
  You can balance research ambition with practical constraints around cost, latency, reliability, data availability, and customer requirements
• Ownership Mentality:
  You take responsibility for whether post‑training work improves real system outcomes, not just offline scores