Senior Research Engineer
FAR.AI
Summary
This role would be a good fit for an experienced machine learning engineer, or an experienced software engineer looking to transition to AI safety research.
Job description
About the Role
This role would be a good fit for an experienced machine learning engineer, or an experienced software engineer looking to transition to AI safety research.
Responsibilities
- Join one of our existing workstreams and lead projects there
- Detecting and preventing deception: large-scale training of transformers
- Preventing catastrophic misuse: detect and mitigate vulnerabilities and other risks in frontier AI models, with a focus on technical leadership
- Accelerating our research: build frameworks and infrastructure to run new experiments more rapidly, with a focus on high-performance computing
- Mentor other engineers or scientists in engineering skills
- Contribute to the growth of the research portfolio
Qualifications
- Significant software engineering experience (prior work experience and open-source contributions)
- Fluency working in Python
- Results-oriented and motivated by impactful research
- Prior experience mentoring other engineers or scientists in engineering skills
- Expertise in one of: Machine Learning, High-Performance Computing, or Technical Leadership
Option 1 – Machine Learning
- Substantial experience training transformers with common ML frameworks like PyTorch or JAX
- Good knowledge of basic linear algebra, calculus, vector probability, and statistics
Option 2 – High-Performance Computing
- Power user of cluster orchestrators such as Kubernetes (preferred) or SLURM
- Experience building high-performance distributed systems (e.g., multi-node training, large-scale numerical computation)
- Experience optimizing and profiling code (ideally including on GPU, e.g., CUDA kernels)
Option 3 – Technical Leadership
- Experience designing large-scale software systems, as an architect in greenfield software development or leading a major refactor
- Comfortable project managing small teams, such as chairing stand-ups and developing detailed roadmaps to execute on a 3-6 month research vision
Requirements
- Significant software engineering experience
- Fluency in Python
- Results-oriented and motivated by impactful research
- Prior experience mentoring other engineers or scientists in engineering skills
- Expertise in Machine Learning, High-Performance Computing, or Technical Leadership
- Substantial experience training transformers with PyTorch or JAX
- Knowledge of linear algebra, calculus, vector probability, and statistics
- Power user of Kubernetes or SLURM
- Experience building high-performance distributed systems
- Experience optimizing and profiling code, ideally on GPU with CUDA kernels
- Experience designing large-scale software systems
- Comfortable project managing small teams and developing roadmaps
Benefits
- Visa Sponsorship
- Work-related Travel Expenses
- Equipment Expenses
- Catered Lunch & Dinner
- Remote Work Possible
Company overview
FAR.AI is an independent nonprofit research organization founded in 2022 and based in Berkeley, California, dedicated to ensuring the safe development and deployment of advanced artificial intelligence systems. The organization focuses on frontier alignment research to make AI systems trustworthy, secure, and beneficial to society. FAR.AI operates through three main divisions: FAR.Research explores high-potential technical AI safety research directions including adversarial robustness, interpretability, and preference learning; FAR.Labs supports the San Francisco Bay Area AI safety research community through coworking spaces, events, and programs; and FAR.Futures delivers initiatives that bring together global leaders in AI academia, industry, and policy. The organization has secured over $30 million in funding from major supporters including Coefficient Giving, Schmidt Sciences, and the AI Safety Fund, enabling significant expansion of research capabilities and field-building initiatives. FAR.AI conducts rigorous technical research on AI safety challenges while building the field through workshops, fellowships, and collaborative initiatives, positioning itself as a critical institution addressing one of the most pressing technological challenges of our time.