Join to apply for the AI Infrastructure Engineer, ML Data Platform role at Scale AI Join to apply for the AI Infrastructure Engineer, ML Data Platform role at Scale AI 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. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:
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