AI Infrastructure Engineer, ML Data Platform

Scale’s AI Infrastructure team supports both R&D and applied Generative AI initiatives, driving breakthroughs in areas of post-training research such as AI safety, agents, and evaluating state-of-the-art model performance.

As a Data Infrastructure Engineer on the AI Infrastructure team, you will design, build, and scale the data platform that powers all R&D and applied ML initiatives at Scale. Collaborating closely with product engineering, platform engineering, and ML researchers, you will build robust and easy-to-use APIs and data pipelines. Your work will play a critical role in advancing frontier ML research, accelerating the data sales cycle, and improving data quality - all while optimizing infrastructure costs.

You will:

• Design, implement, and maintain scalable data platforms to support diverse R&D and applied ML workloads.
• Partner with ML researchers, product engineers, and operations teams to align data infrastructure with organizational goals.
• Collaborate with ML researchers to build data access tools that help advance the state of frontier post-training research.
• Participate in our team’s on call process to ensure the availability of our services.
• Own projects end-to-end, from requirements, scoping, design, to implementation, in a highly collaborative and cross-functional environment.

Ideally you'd have:

• 2+ years of experience in building and operating large-scale distributed data systems that support ML workloads.
• Expertise in modern data platform technologies.
• Experience working with standard containerization & deployment technologies like Kubernetes, Helm, Terraform, Docker, etc.
• Strong problem solving skills and the ability to work effectively in a fast paced, dynamic environment.

Nice to haves:

• Familiarity with ML development tools such as PyTorch, HuggingFace, or Weights & Biases.
• Experience with a variety of storage systems: object (S3), document (MongoDB), relational (Postgres), and distributed (Redis, Elasticsearch).
• Exposure to orchestration platforms like Temporal, Airflow, or AWS Step Functions.
• Experience supporting post-training workflows such as evaluation, fine-tuning, and RLHF in LLM systems.
• Experience working in a fast-moving startup or high-scale ML infra environment.