Member of Technical Staff, AI Engineering (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 to help build the data systems that make frontier model training possible. The data Handshake builds for and acquires on behalf of labs is getting more complex and more sensitive and this role is responsible for improving how we generate process and prepare that datawhether that means building higher-quality synthetic and LLM-generated training data or making acquired third-party data safe to use by removing personal information while preserving the structure that makes it valuable.

You will partner with researchers domain experts legal/compliance stakeholders and customers to turn ambiguous data questionsabout generation quality evaluation or privacyinto experiments pipelines and durable products. Early members of the team will have unusual influence over our technical direction standards and culture.

Location: Bangalore

• Design and build systems that improve the quality scale and safety of the data Handshake generates and acquires for frontier model trainingspanning synthetic data generation and data anonymization/PII removal.

• Translate ambiguous research partner or compliance needs into clear hypotheses experiments evaluation plans and production-quality implementations.

• Build and improve data-processing pipelines evaluation frameworks benchmarks and quality-control systems whether the goal is generating higher-signal synthetic data or verifying that sensitive data has been properly de-identified.

• Run fast rigorous iteration loops: prototype evaluate interpret results and turn learnings into the next system or product.

• Partner directly with researchers domain experts andwhere relevantlegal and compliance teams to ensure data is both high-utility and responsibly handled.

• 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.

• 210 years of recent demonstrated experience in one or more of: synthetic/LLM-generated data post-training and model-evaluation work privacy engineering or data anonymization/de-identification at scale.

• A hands-on individual contributor track recordthis is not a team-lead or engineering-management role.

• Strong Python skills and the ability to write clean efficient scalable software for large messy real-world datasets.

• Sound judgment for reasoning about data quality risk and utilityforming hypotheses choosing meaningful metrics diagnosing failures and distinguishing signal from noise.

• Experience designing systemsnot only implementing specificationsincluding 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 synthetic or LLM-generated data pipelines for model training.

• Building or operating large-scale data de-identification or anonymization systems ideally involving relational or graph-structured data with experience preserving referential/relationship integrity after anonymization.

• 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.

• Prior work in a regulated or high-sensitivity data environment (healthcare finance HR/people data government) or experience with re-identification risk assessment and privacy auditing.

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

Perks:

• 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.

Required Experience:

IC