Software Engineer, ML Dev Enablement

Mission Summary:

We are looking for a Software Engineer to join our ML Infrastructure: Dev Enablement Team. Our mission is to build a frictionless development environment that empowers our researchers and engineers to rapidly innovate on deep learning models for autonomous driving.

We manage a high-scale Cloud Development Environment (CDE) platform that provides standardized, high-performance workspaces for ML development. As we evolve, in this role, you’ll spearhead high-impact initiatives: designing multi-cloud setups to maximize GPU availability, driving deep-level model optimization, and building next-generation Agentic AI toolings. You will play a pivotal role in ensuring our training ecosystem remains cutting-edge, resilient and highly efficient.

What You’ll Be Doing:

• Build Agentic AI Tooling: Design, develop, and enhance Agentic AI tools and systems to automate workflows, streamline the ML lifecycle, and empower developer productivity.
• Scale Core Infrastructure: Drive the continuous development of our core ML infrastructure and existing CDE platform, leveraging Kubernetes to build robust, high-scale distributed solutions.
• System-Level ML Optimization: Partner closely with ML Researchers to profile and optimize distributed training jobs (PyTorch/DDP) and data pipelines. Focus on resolving system-level bottlenecks—such as data loading (I/O), memory management, and network communication overhead—to maximize GPU utilization and training throughput.
• Collaborate Cross-Functionally: Partner with ML engineers and data scientists to understand their complex needs, bridging the gap between underlying infrastructure and model development.

What We’re Looking For:

• BS or MS in Computer Science or related field
• Strong knowledge of software engineering principles and distributed systems.
• Strong proficiency with Python or Go or C++
• Experience with building on AWS services or other Cloud platforms and container orchestration using Kubernetes.
• Experience with the various stages of the ML development lifecycle

Bonus Points:

• Hands-on experience with ML model profiling and performance optimization for distributed training.
• Experience managing or working with high-performance compute resources (GPUs).
• Experience with ML frameworks such as PyTorch or Ray.
• Experience building, integrating, or enhancing Agentic AI systems and LLM-driven developer tools.