• Beacon Biosignals is seeking a talented MLOps Engineer as part of Beacon's Quantitative Tools Team to accelerate research & development for neurobiomarker discovery to power novel therapies for sleep disorders, neurologic conditions, and psychiatric disease. • In this role, you'll build and maintain tooling that supports the ability of algorithm development teams to train, evaluate, deploy, and monitor advanced machine learning and signals processing algorithms to analyze large-scale EEG data. • This role is part of the research software engineering function within the Quantitative Tools Team and reports to the VP of Analytics and Machine Learning at Beacon. • At Beacon, we've found that cultural and scientific impact is driven most by those that lead by example. • As such, we're always seeking new contributors whose work demonstrates an avid curiosity, a bias towards simplicity, an eye for composability, a self-service mindset, and - most of all - a deep empathy towards colleagues, stakeholders, users, and patients. • We believe a diverse team builds more robust systems and achieves higher impact. • Beacon's robust asynchronous work practices ensure a first-class remote work experience, but we also have in-person office hubs available located in Boston, New York, and Paris.

Requirements

• You're comfortable building cloud infrastructure for training and evaluating models on large datasets. • You have experience setting up and maintaining the infrastructure required for serving ML models, and have worked with teams to deploy their models into production. • You have experience with scientific computing in Julia or Python, and writing efficient, maintainable, composable packages in at least one of these languages. • You have basic knowledge of machine learning, signal processing, statistics, and/or optimization. • You're excited to work with large-scale time series biosignal data (e.g. EKG, actigraphy, EEG, polysomnography, etc.). • You're excited to build efficient human-in-the-loop labeling workflows to enable large-scale training data collection. • You're familiar with or excited to work in an environment that makes heavy use of Julia, Python, Docker, K8s, Helm, workflow orchestration tools (e.g. Argo, Airflow, Ray), Kafka, SQL, GraphQL, and GitHub Actions. • You have excellent verbal/written communication and presentation skills. • You're comfortable working in a highly asynchronous hybrid environment, and have demonstrated success doing so in the past. • Approximate experience: PhD + 1-4 years experience, Master's + 3-7 years experience, Bachelor's + 4-9 years experience, or other comparable experience.

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