Research Engineers, Post-Training

Key Responsibilities

• Design and run post-training workflows that improve the behavior, reliability, and usefulness of AI systems
• Create 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

Who You Are

• 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 Towards 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

What We Offer

• The base salary range for this role is $150K – $250K, depending on experience, location, and level
• 100% covered medical, dental, and vision for employees and dependents
• 401(k) with additional perks (e.g., commuter benefits, in‑office lunch)
• Access to state-of-the-art models, generous usage of modern AI tools, and real-world business problems
• Ownership of high-impact projects across top enterprises
• A mission-driven, fast-moving culture that prizes curiosity, pragmatism, and excellence