Software Engineer, ML Systems

About the Role

We are looking for a pragmatic, Software Engineer to own the productionization of our research pipelines. This is an implementation-heavy role designed for an engineer who can take a nascent research idea and build the robust, scalable machinery required to prove it at scale within our cloud infrastructure.

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).