Software Engineer, ML Systems

Key Responsibilities

• Pipeline Engineering: Build and manage end-to-end ML pipelines (ETL and automated evaluation) that are the bedrock of our RL research. - Bottleneck Resolution: Identify and refactor inefficient research code. You act as the primary engineer ensuring that a promising idea reaches its full potential through scalable code. - Standardization: Establish best practices for versioning, experiment tracking, and CI/CD for ML models to ensure reliability. - Cloud Infrastructure & Observability: Manage the deployment and scaling of workloads on Kubernetes. Implement the tooling and telemetry that allows the team to understand agent behavior and training health at a glance. ## Minimum Qualifications

• BS in Computer Science, a related technical field, or equivalent industry experience

• 2+ years of relevant industry experience

• Expert-level Python skills and a disciplined approach to software engineering (testing, versioning, and modular design). - Experience building and managing end-to-end ML pipelines in a production or research-intensive environment. ## Preferred Qualifications

• Full-stack ML experience: Comfortable moving from data engineering to model debugging. - Experience refactoring research-grade code into high-quality, scalable production packages. - Proven ability to design and implement complex data-loading and evaluation systems for non-deterministic models. - Experience with workflow orchestration tools (e.g., Kubeflow, Airflow, or Metaflow). - Experience managing large-scale experiments on cloud providers (AWS, GCP, or Azure). - Proven track record collaborating directly with researchers to translate algorithmic requirements into engineering roadmaps. - Hands-on experience with containerization (Docker) and orchestration (Kubernetes). ###