Research Engineer, Platform

Research Engineers on the Platform team at Basis advance research methods and package them into reusable modules that others can build on. You will develop Basis core technology modules (fundamental algorithmic components like ChiRho, Effectful, Weighted), contribute research-driven capabilities to commercial platform offerings, and ensure research advances have clear paths to impact-whether commercial or societal.

We are looking for people who bridge research excellence and engineering rigor. The ideal Research Engineer has both published research and shipped production code, understands how to translate experimental techniques into robust implementations, and thinks carefully about software architecture that enables others to build on your work.

You will identify high-value research problems aligned with Basis mission and develop them from ideation through concrete implementation.

This role is distinguished from Operations Research Engineers (who focus on internal tooling) by emphasis on advancing research methods for platform and commercial applications. You will work on Core Tech Module Teams and contribute to research that defines Basis's technological foundation.

We seek individuals who excel technically and value probing concepts at their foundations. Our research engineers aspire to do rigorous, high-quality, robust science and engineering, but are not afraid to tinker, make mistakes, and explore radically different ideas to get there.

- Have demonstrated ability to conduct research that is of high quality . Possible ways to demonstrate this include:

- Publications at top-tier conferences (NeurIPS, ICML, ICLR, POPL, PLDI, OOPSLA)

- Technical reports or preprints showing novel research contributions

- Open-source research projects with significant adoption or citations

- Have demonstrated ability to drive software projects from start to finish . This could be evidenced by:

- Research code released as production-quality libraries or frameworks

- Contributions to major open-source projects (PyTorch, TensorFlow, JAX, or equivalents)

- Systems built that span research prototypes through deployable implementations

- Possess deep knowledge of relevant technical areas including probabilistic programming, causal inference, program synthesis, neural architectures, or related fields central to Basis research directions.

- Be proficient in research engineering tools including Python, PyTorch/JAX, version control, testing frameworks, documentation systems, and software engineering practices that make research code maintainable and extensible.

- Understand paths from research to impact . You think about how research advances can translate to commercial applications, platform capabilities, or open-source contributions that benefit broader communities.

- Value software quality and reusability . You design modules that others can build on, write comprehensive documentation and tests, and architect systems that remain coherent as they evolve.

- Progress with autonomy and intellectual curiosity . You can identify valuable research directions, design experiments, implement solutions, and evaluate results without extensive direction.

- Be excited about solving real-world problems and having positive societal impact through research that advances our understanding of intelligence and our ability to tackle intractable challenges.

- PhD in Computer Science, Machine Learning, Statistics, or related field with publications at top-tier venues.

- Experience at research organizations that successfully productionized innovations (Bell Labs, PARC, Microsoft Research, Google Research, Meta FAIR, DeepMind).

- Contributions to widely-used research libraries or frameworks.

- Background spanning multiple areas (ML + PL, causal inference + probabilistic programming, theory + systems).

- Experience with Bayesian methods, probabilistic programming languages, or causal modeling.

- Track record of research that influenced commercial products or open-source ecosystems.

- Advance research methods in areas aligned with Basis mission including probabilistic programming, causal inference, reasoning under uncertainty, program synthesis, and foundations of machine learning.

- Build Basis core technology modules producing fundamental algorithmic components that provide reusable capabilities across multiple projects and applications.

- Package research into production-ready code by translating experimental techniques into robust implementations with comprehensive documentation, tests, and examples that enable others to build on your work.

- Contribute to Platform development by developing research-driven capabilities that enhance commercial offerings, integrating novel techniques into platform APIs and features.

- Identify high-value research problems by staying connected to research frontiers, understanding commercial and societal needs, and recognizing opportunities where Basis capabilities can make significant impact.

- Develop new project concepts from ideation through concrete vision, scoping research questions, defining technical approaches, and creating compelling cases for new initiatives.

- Collaborate across research and engineering teams to ensure research advances integrate smoothly with platform infrastructure and that engineering constraints inform research directions.

- Ensure research has path to impact whether through commercial deployment, open-source release, academic publication, or transfer to other projects within Basis portfolio.

- (Optionally) Publish and present findings in journals and conferences to contribute to broader research community and establish Basis thought leadership.

- Contribute to the culture and direction of Basis by modeling excellence at the intersection of research and engineering, rigorous thinking about foundations, and commitment to building tools that advance societal capability.