Research Engineers, 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

Requirements

• 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

Benefits

Additional Information

About Distyl AI Distyl is an applied AI technology company partnering with the world's most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations. We research and deploy technologies that power AI-native operations - both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations - from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys. Distyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s. The results reflect this approach: a 100% production deployment success rate for our customers and one of the few enterprise AI companies to run a profitable business.

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