Member of Technical Staff, AI Engineering (India)

Handshake; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Benefits offered by this job

Equity grant

Housing bonus

Performance bonus

Medical insurance

Food credit

Job summary

Handshake is hiring a Member of Technical Staff to build data systems for frontier model training. You will design data pipelines, implement privacy-preserving data processes, and collaborate with researchers, legal/compliance teams, and customers in a fast-moving environment.

You will partner with researchers and domain experts in Bengaluru to turn ambiguous data questions into experiments, pipelines, and durable software that improves AI systems and data safety.

Qualifications

• Hands-on contributor with 2–10 years in synthetic/LLM-generated data, post-training or privacy engineering.
• Strong Python skills and ability to write clean, scalable software for large datasets.
• Experience designing systems and data-processing pipelines with reliable, reusable components.
• Ability to translate research, partner, or compliance needs into experiments and production-grade implementations.
• Comfort in an ambiguous, fast-moving environment with ownership and collaboration.

Responsibilities

• Design and build data systems that improve data quality, scale, and safety for frontier model training.
• Translate research and compliance needs into hypotheses, experiments, and production-ready implementations.
• Develop and improve data-processing pipelines, evaluation frameworks, and quality-control systems.
• Run rapid iteration loops: prototype, evaluate, and turn insights into new systems or products.
• Collaborate with researchers, domain experts, and legal/compliance teams to ensure high-utility, privacy-safe data.

Skills

Python

Data privacy

Data quality

Data pipelines

Job description

About Handshake

Handshake's 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 Palantirâand help build the systems that make expert human knowledge useful for advancing AI.

About Handshake Labs

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 weeksâwith expert review and calibration built into the system.

The Role

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 dataâwhether 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 questionsâabout generation, quality, evaluation, or privacyâinto experiments, pipelines, and durable products. Early members of the team will have unusual influence over our technical direction, standards, and culture.

Location: Bangalore

What you'll do

• Design and build systems that improve the quality, scale, and safety of the data Handshake generates and acquires for frontier model trainingâspanning 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, andâwhere relevantâlegal 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.

What we're looking for

• 2â10 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 recordâthis 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 utilityâforming hypotheses, choosing meaningful metrics, diagnosing failures, and distinguishing signal from noise.

• Experience designing systemsânot only implementing specificationsâincluding 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.

Especially compelling experience

• 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.

Why join

• 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.