Machine Learning Software Engineer, Libraries

About the Role

Key Responsibilities

• Collaborate with product managers and library users to translate functional requirements into robust technical specifications and production-ready code.
• Work closely with research scientists to productionize novel models and algorithms, integrating them into core libraries.
• Contribute high-quality, tested, documented, and maintainable code across the library stack, ensuring reliability and ease of use for other engineers.
• Champion software engineering excellence across the team and company, driving best practices in testing (unit, integration, performance), CI/CD, and system architecture.
• Own work end-to-end, from ideation and experimentation through deployment and maintenance in a small, agile team environment.
• Continuously explore and evaluate new techniques and frameworks to guide the technical evolution of the platform.
• Share knowledge with the team, contribute to the engineering guild, and stay connected with the broader community.

Requirements

• A passion for the craft of software engineering and building robust, maintainable systems in a collaborative, cross-functional team.
• Solid software engineering skills, with a focus on writing clean, testable, and maintainable code.
• Strong problem-solving skills, with the ability to diagnose complex issues in a high-uncertainty R&D environment.
• A commitment to owning the software development lifecycle, including testing, containerization, and CI/CD.
• A deep understanding of machine learning fundamentals (e.g., model architectures, optimizers, statistical principles) and the data science lifecycle.
• Strong proficiency in Python's scientific computing and Machine Learning ecosystem (e.g., PyTorch, NumPy, SciPy, Pandas).
• An understanding of how high-level Machine Learning frameworks interact with low-level hardware (e.g., C++/CUDA on GPUs).
• A proactive ownership mindset and the ability to navigate ambiguity.
• Excellent collaboration and communication skills, with an ability to discuss complex technical concepts with both researchers and product teams.

Nice to Have

• Professional experience with C++ and/or GPGPU programming (e.g., CUDA, Triton) for performance optimization.
• Experience building and maintaining widely-used internal or open-source libraries.
• Experience applying machine learning to scientific domains, particularly with 3D geometric or simulation data.
• Familiarity with model packaging and serialization techniques (e.g., ONNX, TorchScript).

Qualifications

• Experience with Python's scientific computing and Machine Learning ecosystem (e.g., PyTorch, NumPy, SciPy, Pandas).
• Understanding of high-level Machine Learning frameworks and low-level hardware interaction (e.g., C++/CUDA).

Benefits & Perks

• Equity options - share in our success and growth.
• 5% 401(k) match - invest in your future.
• Flexible working - balance your work and life in a way that works for you.
• Hybrid setup - enjoy our Manhattan office while keeping remote flexibility.
• Enhanced parental leave - support for life's biggest milestones.
• Private healthcare - comprehensive coverage for you and your family.
• Personal development - access learning and training to help you grow.
• Work from anywhere - extend your remote setup to enjoy the sun or reconnect with loved ones.