Member of Technical Staff, AI Engineering (India)
Salary not listed • India • Full-time
Posted 21d ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
LLM Engineer
About the job
Handshake AI works directly with frontier labs on their most consequential data, evaluation, and post-training challenges, building systems that turn expert human knowledge into data and evaluations that improve frontier models. Handshake Labs is building external AI products, research platforms, and customer-facing AI systems, evolving custom-built work into reusable products and platforms. This role focuses on building the data systems essential for frontier model training, improving how we generate, process, and prepare complex and sensitive data, whether through synthetic data generation or anonymization while preserving structure. You will partner with researchers, domain experts, legal/compliance stakeholders, and customers to translate ambiguous data questions into experiments, pipelines, and durable products, with significant influence over technical direction, standards, and culture.
Responsibilities
- Design and build systems to improve the quality, scale, and safety of data for frontier model training, including synthetic data generation and 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.
- Run iteration loops involving prototyping, evaluation, interpretation of results, and implementation of learnings into systems or products.
- Partner with researchers, domain experts, and legal/compliance teams to ensure data utility and responsible handling.
- Identify repeatable patterns and productize them into reusable software and platforms.
- Raise the technical bar through strong design judgment, clear communication, code quality, and mentorship.
Requirements
- 2-10 years of experience in synthetic/LLM-generated data, post-training and model-evaluation, privacy engineering, or data anonymization/de-identification at scale.
- Proven track record as a hands-on individual contributor.
- Strong Python skills for writing clean, efficient, scalable software for large datasets.
- Sound judgment for reasoning about data quality, risk, and utility, including forming hypotheses, choosing metrics, and diagnosing failures.
- Experience designing systems, considering tradeoffs around quality, scale, reliability, and reuse.
- Comfort operating in an ambiguous, fast-moving environment with substantial ownership.
- Collaborative communication skills for working effectively with diverse stakeholders.
Benefits
- 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