Research Engineer, Post-Training

Key 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

Requirements

• Experience improving model behavior through fine-tuning, preference optimization, reinforcement learning, reward modeling, synthetic data, evals, or related post-training techniques
• Strong programming and experimentation skills to build training and evaluation pipelines, run controlled experiments, analyze results, and iterate quickly
• Research-oriented builder mindset focused on understanding why behavior changes
• Understanding that model behavior is shaped by data, prompts, tools, retrieval, evaluators, and deployment context
• AI-native working style using AI tools daily to accelerate coding, analysis, debugging, experimentation, and research exploration
• Bias towards measurement through evaluations, comparisons, regression tests, and production-relevant metrics
• Comfort balancing research ambition with practical constraints around cost, latency, reliability, data availability, and customer requirements
• Ownership mentality for whether post-training work improves real system outcomes

Nice-to-Haves

• Experience with applied constraints in enterprise environments
• Ability to communicate model behavior and tradeoffs to stakeholders

Compensation & Benefits

• Base salary range: $150K – $250K
• Meaningful equity
• 100% covered medical, dental, and vision for employees and dependents
• 401(k) with commuter benefits and in-office lunch
• Access to state-of-the-art models and modern AI tools