Machine Learning Systems Engineer (Python/Rust)

Location: Remote in Europe or Israel

Company

hirify.global is an AI company developing NEXUS, a large tabular model for enterprise decision-making.

What you will do

• Develop and optimize a large neural network-based tabular model in Python.
• Profile training and inference pipelines to identify performance bottlenecks.
• Rewrite performance-critical components in Rust using PyO3 or custom extensions, with C++ as a secondary option.
• Improve memory efficiency, latency, and throughput across model pipelines.
• Ensure correctness, numerical stability, reproducibility, testing, and documentation.
• Collaborate with ML researchers to productionize new capabilities and shape ML system architecture.

Requirements

• Expert-level Python and Rust skills with strong software engineering hirify.globals.
• 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 and performance-critical code optimization.
• Strong CPU, memory, and latency profiling and debugging skills.

Nice to have

• Experience with tabular ML approaches, including transformers, tree/neural-network hybrids, or learned embeddings.
• Working proficiency in C++ and experience with PyBind11, Cython, or custom extensions.
• Familiarity with PyTorch internals, custom operations, training-loop optimization, or inference engines.
• Background in numerical computing, systems programming, distributed training, batching, or caching.
• Experience with Rust async, SIMD, or parallelism ecosystems such as Tokio, Rayon, or ndarray.

Culture & Benefits

• Competitive compensation with 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 focused on ownership, diversity of thought, and bias toward action.