Senior AI Research Engineer, Handshake

• This role is ideal for someone who thrives at the intersection of research and large-scale engineering, with the ability to translate complex research insights into robust, production-grade systems
• Advanced proficiency in Python with a track record of building clean, maintainable, and performant codebases
• 5+ years of experience in applied ML, large-scale distributed systems, or post-training infrastructure (RLHF, DPO, constitutional AI, etc.)
• Strong expertise with PyTorch and modern ML training frameworks; familiarity with distributed training and inference optimization
• Proven experience designing and operating data pipelines, benchmark frameworks, or large-scale evaluation systems
• Ability to drive technical projects end-to-end: from architecture design to implementation, scaling, and monitoring
• Clear and confident communication skills; ability to collaborate across research and engineering disciplines and influence technical direction

Desirable

• This is a unique opportunity to join a fast-growing team shaping the future of AI through better data, better tools, and better systems—for experts, by experts
• As a Senior Research Engineer, you’ll play a leading role in designing and scaling the infrastructure, systems, and frameworks that power the next generation of LLM post-training and evaluation
• You’ll work closely with research scientists to define methodologies, push the boundaries of data quality and benchmark design, and mentor other engineers to raise the technical bar across the team
• Architect, implement, and optimize large-scale post-training systems and data processing pipelines, ensuring reliability, scalability, and performance
• Lead the development of next-generation LLM benchmarks and evaluation frameworks, defining standards for measuring advanced reasoning, alignment, and knowledge capabilities
• Design and enforce rigorous methodologies for verifying data integrity and quality across highly specialized datasets
• Drive software/hardware performance optimization to accelerate experimentation and deployment (e.g., memory usage, training throughput, distributed systems)
• Partner with cross-disciplinary teams—including research scientists, domain experts, and product engineers—to validate and productionize model improvements
• Mentor junior engineers and shape technical best practices for the post-training and evaluation engineering pod
• Influence long-term research engineering strategy by identifying opportunities to systematize evaluation and data quality at scale