Join Handshake as a Member of Technical Staff to build critical data systems for frontier AI training in Bangalore. Work on synthetic data generation, anonymization, and evaluation pipelines with top-tier researchers and engineers.

About the Company

Handshake is a company dedicated to organizing expert human knowledge to advance the AI economy. Handshake AI collaborates directly with frontier labs, tackling their most critical data, evaluation, and post-training challenges. The team builds sophisticated systems that transform expert human knowledge into the high-quality data and evaluations required to improve frontier models. Handshake Labs, a division within the company, focuses on creating external AI products, research platforms, and customer-facing AI systems. The work involves designing evaluations and training environments, building robust data and feedback systems, executing experiments, and turning successful approaches into reusable infrastructure. The team comprises engineers, researchers, and operators from top organizations like Scale AI, Meta, Google, Amazon, xAI, Notion, and Palantir, providing a unique environment to influence the direction of AI development.

Role Overview

We are hiring a Member of Technical Staff (MTS) to join the AI Engineering team in Bangalore. In this role, you will be responsible for building the data systems that enable frontier model training. The data Handshake generates and acquires for labs is becoming increasingly complex and sensitive. Your primary focus will be on improving how data is generated, processed, and prepared—whether by creating higher-quality synthetic and LLM-generated training data or by making acquired third-party data safe to use through anonymization and PII removal while preserving its structural value. This is an individual contributor role, not a management position. You will collaborate with researchers, domain experts, legal and compliance stakeholders, and customers to convert ambiguous data-related questions about generation, quality, evaluation, or privacy into experiments, pipelines, and durable products. As an early team member, you will have significant influence on technical direction, standards, and culture.

Key Responsibilities

• Design and build systems that improve the quality, scale, and safety of data for frontier model training, covering synthetic data generation and data anonymization/PII removal.
• Translate ambiguous research, partner, or compliance needs into clear hypotheses, experiments, evaluation plans, and production-ready implementations.
• Develop and enhance data-processing pipelines, evaluation frameworks, benchmarks, and quality-control systems for generating high-signal synthetic data or verifying proper de-identification of sensitive data.
• Run fast, rigorous iteration loops: prototype, evaluate, interpret results, and incorporate learnings into subsequent systems or products.
• Partner directly with researchers, domain experts, and relevant legal/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.

Tech Stack

• Programming Languages: Python (primary)
• Data & AI Technologies: Synthetic data generation, LLM-generated data pipelines, data anonymization/de-identification, PII removal, evaluation frameworks, LLM/agent benchmarks, post-training and model evaluation, reinforcement learning, alignment, human-in-the-loop systems.
• Tools & Infrastructure: Data-processing pipelines, quality-control systems, large-scale data systems, SQL (implied), relational/graph-structured data handling.
• Additional: Experience with privacy engineering, re-identification risk assessment, and privacy auditing; familiarity with regulated data environments (healthcare, finance, HR, government).

• 2–10 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 record—this is not a team-lead or engineering-management role.
• Strong Python skills and ability to write clean, efficient, scalable software for large, messy, real-world datasets.
• Sound judgment for reasoning about data quality, risk, and utility.
• Experience designing systems, including tradeoffs around quality, scale, reliability, and reuse.
• Comfort operating in an ambiguous, fast-moving environment with substantial ownership.
• Collaborative, low-ego communication and ability to work effectively with researchers, engineers, domain experts, and customers.
• Especially compelling: building large-scale synthetic or LLM-generated data pipelines; large-scale data de-identification/anonymization systems; developing LLM/agent benchmarks or evaluation methodologies; research on RL, alignment, synthetic data; experience in regulated data environments; published research or open-source contributions.

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.