Software Engineer, ML Dev Enablement

About the Role 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 Architect Multi-Cloud Solutions: Explore, design, and implement multi-cloud architectures for our ML training platform to increase the availability, scalability, and resilience of high-performance compute resources (GPUs).

System-Level ML Optimization: Partner closely with ML Researchers to profile and optimize distributed training jobs (PyTorch/DDP). 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.

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.

Collaborate Cross-Functionally: Partner with ML engineers and data scientists to understand their complex needs, bridging the gap between underlying infrastructure and model development.

Drive Engineering Excellence: Champion best practices in software engineering, system reliability, and code quality while mentoring junior engineers through high-level system design and code reviews.

What We're Looking For BS or MS in Computer Science or related field

2+ years of professional experience in software engineering with strong foundations in distributed systems.

Expertise with Python or Go or C++

Solid 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

Strong written and oral communication skills, with a track record of successfully taking ownership of infrastructure components and mentoring junior engineers.

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.