Basis Research Institute
Research Engineer, Platform
About this role
Basis seeks a Research Engineer to develop core AI technology modules and translate research advances into reusable, production-quality platform components. This role combines research excellence with software engineering rigor, focusing on probabilistic programming, causal inference, and related areas that form Basis's technological foundation.
What you'll do
- Develop and maintain core technology modules (ChiRho, Effectful, Weighted) as reusable research components
- Translate experimental research techniques into robust, production-quality implementations
- Conduct high-quality research aligned with Basis mission and evaluate impact pathways
- Design software architecture that enables other researchers and engineers to build upon your work
- Contribute research-driven capabilities to commercial platform offerings
- Collaborate with internal teams and external partners on research problems
What they're looking for
- Research publication and peer review experience at top-tier venues
- Python proficiency with PyTorch/JAX
- Probabilistic programming or causal inference expertise
- Software engineering practices (version control, testing, documentation)
- Open-source project contribution or maintenance
- Program synthesis or neural architecture knowledge
- System design and software architecture
- Scientific writing and technical communication
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Basis Research Institute
Basis Research Institute is a nonprofit AI research organization building trustworthy data infrastructure, scalable ML training systems, custom robotic platforms, and LLM-powered automation tools to support both research and commercial applications. The organization is hiring Data Engineers, ML Systems Engineers, Hardware Systems Engineers, Software Engineers, and Machine Learning Research Engineers to develop its technical infrastructure and translate research into production systems.
View all jobs at Basis Research InstituteLikely interview questions
- Tell us about a research project you've published and then turned into production-quality code or an open-source library. What were the key challenges in that transition?
- Describe your experience with probabilistic programming, causal inference, or program synthesis. What specific problems have you tackled in these areas?