Applied AI Engineer
Fundamental5 months ago
Remote, EMEASenior
Responsibilities
- Develop and optimize a large neural network-based tabular model in Python.
- Profile training and inference pipelines to identify performance bottlenecks.
- Rewrite critical Python-limited components in Rust using PyO3 or custom extensions, with C++ as a secondary option.
- Improve memory efficiency, latency, and throughput across model pipelines.
- Ensure model correctness, numerical stability, and reproducibility.
- Collaborate with ML researchers to productionize new capabilities.
- Maintain clean abstractions, comprehensive tests, and clear documentation.
- Shape architectural decisions for ML systems handling tabular data.
Requirements
- Expert-level Python and Rust skills with strong software engineering fundamentals.
- Hands-on experience bridging Python and Rust using PyO3, maturin, or custom extensions.
- Experience developing and maintaining ML models in production.
- Strong understanding of neural networks.
- A track record of optimizing performance-critical code.
- Strong CPU, memory, and latency profiling and debugging skills.
- Preferred experience includes tabular ML, C++, Python-C++ bridging, PyTorch internals, custom operations, training or inference optimization, numerical computing, systems programming, large-scale ML infrastructure, Rust async programming, and SIMD or parallelism crates.
Benefits
- Competitive compensation including salary and equity.
- Comprehensive health coverage for employees and dependents.
- Paid parental leave for all new parents, including adoptive and surrogate journeys.
- Relocation support for employees moving to an office location.
- Mission-driven, low-ego culture valuing diversity of thought, ownership, and bias toward action.
Categories
About Fundamental
Fundamental builds NEXUS, a large tabular AI model and platform for enterprise decision-making on structured data, delivered with deployment support. It sells to large companies, including Fortune 100 firms, through software licensing and forward-deployed data science services. Founded in 2024 by former DeepMind engineers and headquartered in San Francisco, the company is expanding industry use cases such as oil and gas, where it supports predictive analytics and operational planning.