Job Description

Machine Learning Engineer at MLCommons Association About MLCommons Association At MLCommons, we democratize AI through open, state-of-the art industry-standard benchmarks and data tooling to measure quality, performance, and risk. We’re a global, non-profit consortium supported by 125+ members and affiliates, including startups, leading companies, and academics. We are a fully remote organization that supports a flexible working environment; this requires mature communication, accountability, and transparent collaboration by all team members. About AI Risk & Reliability AI Risk & Reliability works to support and inform the global discussion around AI safety and enable responsible development and deployment of AI systems that deliver value to users. With the AILuminate benchmark, we aim to combine the best innovations from research into AI safety into a production quality benchmark that is technically rigorous, consensus-driven, relevant, informative, and most importantly – practical. Scope of Machine Learning Engineer. We are looking for an ML engineer to help us advance and scale our benchmarking capabilities through the design and creation of an improved platform for benchmark execution. You will work with ML scientists, software engineers, domain experts, and our partners at frontier AI companies to figure out and build the best way forward for our next generation MLOps platform. The Machine Learning Engineer role sits within the AI Risk & Reliability Working Group and reports to the Director of Engineering. This role necessitates at least 40 hrs/wk and is fully remote with working hours that partially overlap with the Pacific time zone. Specific responsibilities: Learn and execute our existing processes and pipelines Work with scientists and engineers to understand our current and future needs Research and propose an MLOps platform migration strategy (includes curating OTS tooling and custom built components) Design and execute this platform migration in collaboration with other engineers as well as highly technical volunteers Create MLOps pipelines (include ETL, transformation, training, and inference components) in our new platform Manage cloud computing infrastructure Perform data engineering for data as needed and produced by our platform Create high quality documentation for the purposes of collaboration and communication Present progress and outcomes to the greater working group Required skills and qualifications for success: A background in ML engineering and MLOps Good communication skills and the ability to work with many different kinds of people Strong experience with Python Experience using a wide variety of general cloud compute and storage resources Strong analytical and problem-solving skills A passion for learning and a strong work ethic Excited about safety and trustworthiness in AI Nice-to-Have skills and qualifications: Experience with supervised fine tuning LLMs (e.g. pytorch, LoRA, PEFT) A masters or PhD in a relevant technical field Compensation & Timeline All team members at MLCommons are non-W2 contractors Compensation commensurate with experience Length of contract: 12 months (with possible extensions) Application Process To apply, please send a resume, any links to profiles you find relevant (e.g. Github, LinkedIn, personal website) and a brief cover letter talking about your qualifications for the responsibilities, as well as contact information. We may eventually ask for professional references that can attest to the quality of your work. Send to jobs@mlcommons.org with the subject line “Applying for Machine Learning Engineer: AIRR.” Seniority level Entry level Employment type Contract Job function Engineering and Information Technology Software Development #J-18808-Ljbffr MLCommons

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