Software Engineer, ML Engineering
Core
Building the technical foundations and infrastructure that power the platform's most demanding capabilities, enabling researchers to iterate reliably and move experiments toward production readiness.
Role type
ML Engineering Infrastructure Engineer
Builds
Training pipelines, data workflows, model integration systems, and tools for rapid experimentation
Domain
Machine Learning Systems, Data Infrastructure, Sensing Technologies, Real-time Inference
Deliverable
production ML models | infrastructure
Required skills
Python, C++/C#/Java/Rust/Go, PyTorch, TensorFlow, ML training pipelines, data workflows, model deployment, inference optimization, MLOps
Preferred skills
Distributed training systems, GPU-accelerated computing, data versioning, experiment tracking, ML metadata management, containerization, orchestration tools, open-source ML contributions
Technologies
PyTorch, TensorFlow, Docker
Responsibilities
Design and build training pipelines, data workflows, and model integration systems; Develop infrastructure that accelerates research iteration; Build systems for data collection, curation, and preprocessing at scale; Create tools and automation that move experiments toward production readiness; Optimize data pipelines for reliability, performance, and observability; Work on model serving infrastructure and integration with the production framework
Seniority
Mid-Senior, hands-on IC