Research Lead
Job description
About the role
This Research Lead position at Far.Ai centers on empirical investigation of AI safety methods. Projects determine whether safety methods meet stakeholder expectations under defined conditions. In this role, you will own the design and execution of empirical studies that interrogate advanced AI systems. You will translate high-level safety questions into measurable outcomes that can be observed and evaluated. Your work will establish the conditions under which specific methods are judged successful or insufficient. You will synthesize complex findings into formats that inform strategic decision-making by technical teams. This role requires you to manage the end-to-end lifecycle of defined research initiatives from scoping to delivery. You will act as a bridge between rigorous analysis and practical implementation in the Berkeley office.
About the company
FAR. AI http://FAR. AI is a non-profit AI research institute working to ensure advanced AI is safe and beneficial for everyone.
Key facts
What you'll do
Datasets and model outputs reveal how safety methods behave, clarifying outcomes for stakeholders. Results from these examinations guide adjustments to safety methods so that stakeholder expectations are met. Defined project scopes shape the design of empirical, scalable ML safety studies that respect set limits. Academic training strengthens the ability to explain complex research concepts and handle intricate analytical tasks. Complex analytical work is handled effectively through academic training that sharpens communication skills. Team collaboration in the Berkeley office drives integrated initiatives that connect research and execution. Integrated initiatives advance when collaboration in the Berkeley office connects research with execution.
You will scope and frame research questions to ensure they are testable and aligned with organizational goals. You will partner with engineers to translate research protocols into operational workflows that are reliable and efficient. You will oversee data collection strategies to ensure that observations are comprehensive and methodologically sound. You will interpret study results to identify patterns, anomalies, and insights regarding system capabilities. You will document procedures and findings to create a clear record that supports reproducibility and review. You will communicate outcomes to diverse audiences, including researchers, product managers, and executives. You will iterate on study designs based on feedback to improve clarity, relevance, and rigor over time.
Requirements
A bachelor's degree is mandatory, with the exact field verified The exact field of the bachelor's degree is confirmed Full-time on-site presence in the Berkeley office is required to support integrated collaboration and initiative success. On-site presence in the Berkeley office is required full-time to enable integrated collaboration and initiative success. Background in grantmaking, convening stakeholders, and pre-deployment testing ensures rigorous validation of methods. Rigorous validation of methods is supported by background in grantmaking, convening stakeholders, and pre-deployment testing.
Practical notes
Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Questions to ask
Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.