Member of Technical Staff, Handshake

• We care more about demonstrated research capability, technical judgment, and a builder’s mindset than a specific title, degree, or career path
• 3+ years of demonstrated strength in post-training, fine-tuning, or model-evaluation work. Relevant experience may include RL, SFT, LoRA/PEFT, full fine-tuning, RLHF, DPO, PPO, reward modeling, or training environments
• Strong Python skills and the ability to write clean, efficient, scalable software
• Hands-on experience with modern ML tooling, particularly PyTorch and large-scale data, training, or evaluation workflows
• Sound experimental judgment: you can form hypotheses, choose meaningful metrics, diagnose failures, and distinguish signal from noise
• Experience designing systems—not only implementing specifications—including the ability to make tradeoffs around quality, scale, reliability, and reuse
• Comfort operating in an ambiguous, fast-moving environment with substantial ownership
• Collaborative, low-ego communication and the ability to work effectively with researchers, engineers, domain experts, and customers

Desirable

• We are hiring a Member of Technical Staff, Post-Training to help define and build this new organization. This is a broad, high-ownership role for researchers who build
• You may come from research science, research engineering, machine learning engineering, or a closely related background; what matters is the ability to reason deeply about model improvement and turn that reasoning into reliable systems
• You will partner with researchers, domain experts, and customers to turn ambiguous post-training questions into experiments, evaluation frameworks, data pipelines, and products
• Arly members of the team will have unusual influence over our technical direction, operating culture, and the reusable systems we build
• Design post-training systems and methodologies for frontier models, including supervised fine-tuning, reinforcement learning, preference optimization, reward modeling, and related approaches
• Translate open-ended research or partner needs into clear hypotheses, experiments, evaluation plans, and production-quality implementations
• Build and improve evaluation frameworks, benchmarks, training environments, data-processing pipelines, and quality-control systems
• Run fast, rigorous iteration loops: prototype, evaluate, interpret results, and turn learnings into the next system or product
• Partner directly with AI researchers and domain experts to develop high-signal data, feedback, and evaluation methods
• Identify repeatable patterns across engagements and productize them into reusable software and platforms
• Raise the technical bar through strong design judgment, clear communication, code quality, and mentorship
• Contribute to the field through benchmarks, open-source tools, research, and technical writing where it creates leverage
• Work on problems at the center of how frontier AI systems improve, alongside leading labs and domain experts
• Help build an early technical organization where your work shapes the roadmap, standards, and culture
• Move fluidly from research insight to real-world systems, with the resources and customer context to see those systems matter
• Join a company building durable infrastructure for careers in the AI economy