Software Engineer, Machine Learning

• Competitive compensation with salary and equity

• Comprehensive health coverage for you and your dependents

• Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys

• Relocation support for employees moving to join the team in one of our office locations

• A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action

• 5+ years of software engineering experience, with meaningful time on ML or data-intensive backends, training pipelines, distributed inference, feature infrastructure, or similar.

• Strong software architecture skills including modular design, clean interfaces, and design patterns for maintainable research codebases.

• Excellent communication skills with a proven ability to discuss complex technical ideas clearly and collaborate effectively across interdisciplinary teams.

• Expert-level proficiency in Python and deep familiarity with PyTorch.

• Hands-on experience with the modern ML research tech stack and cloud infrastructure (e.g., cloud providers (AWS, GCP), Weights & Biases, Datadog, Kubernetes, ArgoCD, GitHub Actions).

• Demonstrated track record of developing and utilizing solutions for robustness and quality assurance within software and/or ML systems.

• Mission-driven mindset, motivated by the prospect of real-world impact and a relentless focus on excellence in software development.

• Familiarity with MLOps best practices, CI/CD for machine learning, and infrastructure-as-code (e.g., Terraform).

• Experience working in an early-stage, fast-paced startup environment.

• A background or strong interest in tabular data, data engineering, or enterprise data architecture.

• Experience with other programming languages (Rust, C++, Go)

• Contributions to open-source machine learning libraries, frameworks, or tools.

• Bachelor's degree in Computer Science, Software Engineering or a STEM field

You will join the Research team as our first software engineer, owning the core codebase behind our model development. The codebase needs to be fast to iterate on, robust through experiments and large training runs, and ready to carry models all the way to production. Working alongside research scientists, you'll find what slows them down and build the infrastructure that fixes it, laying the technical foundations that let Fundamental operate at the frontier of large-scale tabular modeling.

• Amplify and Accelerate: You will be proactive in providing solutions which enable our researchers to be more efficient and effective, including agentic workflows.

• Steward: You will steward the long-term development and maintenance of our codebase. Ensuring it is production-ready, while preserving the flexibility needed for empirically driven research. You will set the bar for code quality by leading pull request reviews and acting as the gatekeeper for external contributions.

• Champion Best Practices: Establish and implement software engineering best practices in the machine learning domain, serving as a mentor and guide to research scientists on the team.

• Elevate Code Maturity & Quality: Lead the transition of our core research codebase from an early-stage experimental state to a highly scalable, maintainable and robust engineering standard. You will proactively manage technical debt, ensure experiment reproducibility, and drive overall code quality.

• Maintain Documentation: Develop and maintain clear, comprehensive documentation for our research infrastructure and systems.

• Collaborate Cross-Functionally: Ensure precise interactions of the research codebase with other code at Fundamental to translate research breakthroughs into tangible, real-world impact.

• Guard the research-engineering contract: Build and own the testing layer that runs alongside model development: CI, contract tests, and verification that catch when research changes break the interfaces engineering depends on. Bugs should surface in PRs, not in production.