Applied AI Engineer
Join Fundamental as an Applied AI Engineer to tackle technical challenges in developing large neural network-based models.
Posted by employer 5 months ago
First seen on Joblaze 1 month ago
Last verified on the company career page 1 day ago
AI in the day-to-day
Fundamental develops NEXUS, a powerful Large Tabular Model for enterprise decision-making.
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Benefits
Joblaze summary
In this role, the Applied AI Engineer focuses on developing and optimizing a large neural network-based tabular model, primarily using Python and Rust. Key skills include strong software engineering fundamentals, experience with machine learning models in production, and proficiency in performance optimization. This position is ideal for someone with a solid background in software engineering and a deep understanding of neural networks, particularly those looking to tackle complex challenges in a pioneering AI company. Fundamental's mission-driven culture emphasizes collaboration and innovation in enterprise decision-making.
Joblaze insights
Quick facts
- What's the tech stack?
- Joblaze extracted these technologies from the posting: C++, PyTorch, Python, Rust.
- What seniority level is this role?
- Fundamental targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Applied AI Engineer role at Fundamental.
From the original posting
Key responsibilities
Take part in development and optimization of a large neural network-based tabular model implemented in Python
Profile training and inference pipelines to identify performance bottlenecks
Rewrite critical components in Rust (via PyO3 or custom extensions) where Python limits us, with C++ (via PyBind11 or custom extensions) as a secondary option where appropriate
Improve memory efficiency, latency, and throughput across model pipelines
Ensure correctness, numerical stability, and reproducibility as the model evolves
Collaborate with ML researchers on productionizing new capabilities
Maintain clean abstractions, comprehensive tests, and clear documentation
Shape architectural decisions for our ML systems handling tabular data
Must have
Strong software engineering fundamentals with expert-level Python and Rust
Hands-on experience bridging Python and Rust (PyO3, maturin, or custom extensions)
Experience developing and maintaining ML models in production
Strong understanding of neural networks
Track record of optimizing performance-critical code
Strong profiling and debugging skills (CPU, memory, latency)
Nice to have
Experience with tabular ML approaches (transformers, tree/NN hybrids, learned embeddings)
Working proficiency in C++ and experience bridging Python and C++ (PyBind11, Cython, or custom extensions)
Familiarity with PyTorch internals or writing custom ops (Rust or C++)
Experience optimizing training loops, data pipelines, or inference engines
Background in numerical computing or systems programming
Exposure to large-scale ML infrastructure (distributed training, batching, caching)
Experience with the Rust async ecosystem (tokio) or SIMD/parallelism crates (rayon, ndarray)
Benefits
Competitive compensation with salary and equity
Standard company text repeated across Fundamental's postings is omitted here.
Similar positions
Explore more