Machine-Learning Research Engineer

These engineers bridge the gap between research and production, translating prototypes into practical systems.

Responsibilities

• Develop and test models: Implement research algorithms, write clean code, and test across various datasets and environments.
• Optimize performance: Improve efficiency through methods such as pruning, quantization, and distributed training.
• Collaborate with researchers: Work closely with LLM scientists, safety researchers, and product teams to integrate novel methods into infrastructure.
• Deploy models: Use MLOps practices to deploy models and monitor their performance.
• Document and maintain code: Write documentation and maintain codebases to enable reproducibility.

Qualifications

• Master's degree in computer science or relevant experience in applied ML.
• Strong programming skills (Python, C++, etc.) and experience with deep learning frameworks.
• Ability to work collaboratively with researchers and engineers.

TAIRC evaluates candidates using role-related evidence and documented review criteria. Employment decisions must not be based on protected personal characteristics.

Engagement Pathways and Compensation Status

TAIRC is currently building its funding base. Opportunities identified as volunteer or unpaid do not provide compensation at this stage. Any future paid appointment would require available funding, organizational approval, applicable legal compliance, and a separate written agreement. Participation does not guarantee future employment or compensation.

• Volunteer contribution interest may be reviewed only as a voluntary, scoped pathway.
• Educational internship interest requires separate supervision, educational purpose, and legal review.
• Advisory expressions of interest are not board or formal advisory appointments.
• Future compensated employment is contingent on funding, approval, legal compliance, and a separate written offer.