Member of Technical Staff, Post-Training (India)

Handshakes mission is to organize expert human knowledge to advance the AI economy. Handshake AI works directly with frontier labs on their most consequential data evaluation and post-training challenges building the systems that turn expert human knowledge into the data and evaluations that make frontier models better.

You will work alongside engineers researchers operators and builders from organizations including Scale AI Meta Google Amazon xAI Notion and Palantirand help build the systems that make expert human knowledge useful for advancing AI.

Handshake Labs is building external AI products research platforms and customer-facing AI systems. We are evolving work that is often custom-built for an individual partner into reusable products and platforms that improve with every deployment.

Our work spans the full post-training loop: designing evaluations and training environments building high-quality data and feedback systems running experiments and turning what works into durable infrastructure. For example we are developing agents that can analyze long complex coding-agent sessions in days rather than weekswith expert review and calibration built into the system.

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. Early members of the team will have unusual influence over our technical direction operating culture and the reusable systems we build.

We care more about demonstrated research capability technical judgment and a builders mindset than a specific title degree or career path.

• 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.

• 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 systemsnot only implementing specificationsincluding 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.

• Building or operating large-scale ML training inference data or evaluation systems.

• Developing LLM/agent benchmarks evaluation methodologies annotation systems or data-quality frameworks.

• Research or applied work on reinforcement learning alignment model behavior synthetic data or human-in-the-loop systems.

• Published research meaningful open-source contributions or evidence of technical leadership in ML systems or AI research.

• Experience productizing research or repeated customer work into robust reusable platforms.

• 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.

• Generous Equity Grant vested over 4 years

• Housing Bonus: 1.3 Lakhs spread throughout the first year

• Well Defined Performance Bonus ranging between 10 - 100% of base

• Medical Insurance Coverage

• Food credit for every in person day.