Member of Technical Staff, AI Engineering (India)

About this job

As a Member of Technical Staff, AI Engineering at Handshake in India, you will focus on building advanced data systems that make frontier AI model training possible. Handshake organizes expert human knowledge to advance the AI economy by working directly with top AI labs on evaluation, post-training, and data challenges. A normal day in this role involves designing and improving data-processing pipelines, developing high-quality synthetic and LLM-generated training data, and building de-identification systems to safely remove personal information from acquired data. You will collaborate closely with researchers, domain experts, and compliance teams to translate complex data requirements into production-quality implementations. Team members handle rapid iteration loops, prototyping solutions, evaluating results, and turning custom solutions into reusable software platforms that drive the AI economy forward.

Responsibilities

• Design and build systems to improve data quality, scale, and safety for frontier model training.
• Translate ambiguous research or compliance needs into hypotheses, experiments, and production implementations.
• Build data-processing pipelines, evaluation frameworks, benchmarks, and quality-control systems.
• Run fast, rigorous iteration loops from prototyping to turning learnings into durable products.
• Partner with researchers, domain experts, and legal teams to ensure data is high-utility and responsibly handled.
• Identify repeatable patterns across engagements and productize them into reusable platforms.
• Raise the technical bar through design judgment, clear communication, code quality, and mentorship.

Requirements

• 2 to 10 years of recent experience in synthetic data, model evaluation, privacy engineering, or data anonymization.
• A hands-on individual contributor track record without engineering management duties.
• Strong Python skills with the ability to write clean software for large, messy datasets.
• Sound judgment for reasoning about data quality, risk, utility, and metrics.
• Experience designing systems with trade-offs around quality, scale, reliability, and reuse.
• Comfort operating in an ambiguous, fast-moving environment with substantial ownership.
• Collaborative and low-ego communication to work effectively with cross-functional teams.

Good to have

• Experience building large-scale synthetic or LLM-generated data pipelines for model training.
• Experience building large-scale data de-identification systems while preserving relationship integrity.
• Developing LLM or agent benchmarks, evaluation methodologies, or annotation systems.
• Research or applied work on reinforcement learning, alignment, or human-in-the-loop systems.
• Prior work in regulated data environments or experience with privacy auditing and re-identification risk.

Who should apply

This role suits experienced software and data professionals with two to ten years of hands-on background in synthetic data, AI model evaluation, or privacy engineering. It is ideal for independent contributors who thrive in ambiguous, fast-moving environments and want to shape technical direction at an early-stage AI company. Candidates must possess strong Python abilities and experience designing robust data pipelines. Please note that this is not an entry-level position; freshers are not mentioned as eligible to apply.

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