Fundamental
AI
SWE(Researchteam)
Neural analysis suggests this role is
optimal for Senior candidates.
“SWE (Research team) at Fundamental. Skills: Large Tabular Model (LTM) development, Foundation model development, Software engineering best practices in ML, Scalable, maintainable, and robust engineering standards, Python, PyTorch, Cloud infrastructure. Owning the core codebase behind model development. Building infrastructure to fix researcher bottlenecks”
What You'll Achieve.
Unlock trillions of dollars of value by giving businesses the Power to Predict; Transform how the world's largest companies make decisions; Operate at the frontier of large-scale tabular modeling; Enable researchers to be more efficient and effective; Translate research breakthroughs into tangible, real-world impact; Catch bugs in PRs, not in production
Industry & Context.
Find what slows researchers down and build the infrastructure that fixes it; Develop solutions for robustness and quality assurance
What They're Looking For.
Must Have
5+ years of software engineering experience, with meaningful time on ML or data-intensive backends, training pipelines, distributed inference, feature infrastructure, or similar, 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
Nice to Have
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 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
What You'll Do.
Owning the core codebase behind model development
Building infrastructure to fix researcher bottlenecks
Laying technical foundations for large-scale tabular modeling
Providing solutions to enable researcher efficiency and effectiveness
Steward long-term development and maintenance of codebase
Ensuring codebase is production-ready while preserving flexibility
Setting bar for code quality
Leading pull request reviews
Acting as gatekeeper for external contributions
Establishing and implementing software engineering best practices in ML
Leading transition of core research codebase to scalable
Proactively managing technical debt
Ensuring experiment reproducibility
Driving overall code quality
Developing and maintaining documentation for research infrastructure and systems
Ensuring precise interactions of research codebase with other code
Building and owning the testing layer that runs alongside model development
How You'll Work.
Team & Collaboration
Working alongside research scientists; Collaborating effectively across interdisciplinary teams; Collaborating cross-functionally; Ensuring precise interactions of the research codebase with other code at Fundamental
Communication Scope
Excellent communication skills; Proven ability to discuss complex technical ideas clearly; Collaborate effectively across interdisciplinary teams
Full Job Description
ABOUT FUNDAMENTAL Fundamental is an AI company pioneering the future of enterprise decision-making. Founded by DeepMind alumni, Fundamental has developed NEXUS – the world's most powerful Large Tabular Model (LTM) – purpose-built for the structured records that actually drive enterprise decisions. Backed by world class investors and trusted by Fortune 100 companies, Fundamental unlocks trillions of dollars of value by giving businesses the Power to Predict. At Fundamental, you'll work on unprecedented technical challenges in foundation model development and build technology that transforms how the world's largest companies make decisions. This is your opportunity to be part of a category-defining company from the ground-up. Join the team defining the future of enterprise AI. ABOUT THE ROLE You will join the Engineering team as our first software engineer embedded within Research, 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. KEY RESPONSIBILITIES - 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 scientis
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