AI Infrastructure Engineer, Model Serving Platform

Scale
San Francisco, CA; New York, NYPosted 29 April 2025

Job Description

Job Application for AI Infrastructure Engineer, Model Serving Platform at Scale AI Back to jobs AI Infrastructure Engineer, Model Serving Platform San Francisco, CA; New York, NY Apply As a software engineer on the ML Infrastructure team, you will work on developing the platform for orchestrating post-training and model evaluation jobs. At Scale, we are constantly developing new data sources and running experiments to understand their impact on ML models. To support this effort, we are looking for engineers who are comfortable navigating cloud infrastructure challenges as well as research challenges in benchmarking and tuning LLMs. The ideal candidate is someone who has strong fundamentals in machine learning, backend system design, and has prior ML Infrastructure experience. They should also be comfortable with infrastructure and large scale system design, as well as diagnosing both model performance and system failures. You will: Develop re-usable platforms for running in-house and open-source LLM-benchmarks. Ensure correctness and performance of post-training and eval jobs on the platform. Improve APIs for managing ML workflows. Contribute to foundational infrastructure at the company for model inference and training. 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: 4+ years of experience developing ML platforms. Passion for working closely with researchers to drive business impact. Experience training and/or benchmarking LLMs. Experience with Python, Docker, Kubernetes, and Infrastructure as code (e.g. terraform). Nice to haves: Experience building, deploying, and monitoring complex microservice architectures. Experience working with a cloud technology stack (eg. AWS or GCP). 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: $175,000 - $220,000 USD PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. About Us: At Scale, we believe that the transition from traditional software to AI is one of the most important shifts of our time. Our mission is to make that happen faster across every industry, and our team is transforming how organizations build and deploy AI. Our products power the world's most advanced LLMs, generative models, and computer vision models. We are trusted by generative AI companies such as OpenAI, Meta, and Microsoft, government agencies like the U.S. Army and U.S. Air Force, and enterprises including GM and Accenture. We are expanding our team to accelerate the development of AI applications. We believe that everyone should be able to bring their who ... (truncated, view full listing at source)
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