Principal Software Engineer

About the role

Key Responsibilities

Design and develop reusable, production-grade Python libraries for AI/ML engineers and data scientists.

Build high-performance native components using Rust, C++, or C where appropriate.

Develop Python bindings and interfaces that are clean, stable, intuitive, and easy for AI/ML teams to use.

Optimize runtime performance, memory utilization, serialization, concurrency, and data movement.

Own library packaging, distribution, dependency management, versioning, and CI/CD processes.

Develop abstractions that simplify complex infrastructure and systems functionality for AI/ML users.

Partner closely with AI/ML engineers and data scientists to understand their workflows, technical requirements, and recurring challenges.

Translate repeated AI/ML workflow patterns into reusable libraries, tools, and developer-friendly abstractions.

Establish and promote standards for library quality, testing, observability, documentation, API design, and adoption.

Troubleshoot and solve complex systems, runtime, and library-level performance challenges.

Mentor engineers and help raise the technical bar across systems engineering, Python development, and AI infrastructure.

Independently drive ambiguous technical initiatives from initial design through implementation and adoption.

Required Qualifications

Staff- or Principal-level software engineering experience.

Deep expertise in Python, particularly reusable library development, API design, and packaging.

Strong systems programming experience with Rust, C++, and/or C.

Experience developing Python extensions, native bindings, or interfaces using technologies such as PyO3, maturin, ctypes, CFFI, nanobind, pybind11, or similar technologies.

Strong understanding of performance profiling, memory management, concurrency, and runtime behavior.

Experience supporting or developing technology for AI/ML, data science, platform engineering, infrastructure, or developer productivity teams.

Sufficient understanding of AI/ML and data science workflows to effectively collaborate with practitioners and design libraries and tools around their needs.

Strong understanding of software testing, documentation, API design, and developer experience best practices.

Ability to independently navigate complex and ambiguous technical challenges and drive solutions from design through production adoption.

Preferred Qualifications

Experience with ML/data libraries, model evaluation, feature pipelines, vector search, data validation, or inference tooling.

Experience with technologies and ecosystems such as Apache Arrow, Polars, NumPy, PyTorch, DuckDB, Ray, or Spark.

Experience designing internal platforms, frameworks, or shared libraries used across multiple engineering or data science teams.

Experience contributing to or maintaining open-source libraries.