AI/ML Engineer

Key Responsibilities - Integrate Foundation Models: Rapidly intake and integrate newly published foundation models (e.g., from academic labs or open-source platforms) into the internal benchmarking system. - Analyze and Troubleshoot: Analyze external repositories with incomplete documentation, work backwards from the code to identify required libraries, and successfully resolve environment issues to make the code functional. - Verify Model Integrity: Verify that imported models are working properly and consistently generating expected outputs. across three dimensions: data, models, and prediction tasks. - Execute Data Engineering: Perform data engineering tasks,, and piping datasets into the benchmarking system. Improve Infrastructure: Execute infrastructure upgrades, such as for backend databases, data and model processing pipelines, optimizations for speed and robustness - Senior Team Members: Act as a junior maintainer by executing tasks and priorities set by senior team members without needing to independently plan the project roadmap. Required Qualifications General Skills & Software Engineering - Exceptional software engineering skills with a strong focus on execution and code maintenance. - Problem-solving: Advanced abilities to troubleshoot and operationalize inherited or undocumented code repositories. - Independent Execution: Proven ability to work independently and execute effectively on directed engineering tasks. Technical Skills - DevOps Capabilities: Strong DevOps skills to assist with model operationalization and infrastructure maintenance. - Data Pipelining: Solid familiarity with data pipelining and general data engineering tasks. - Time Zone Availability: Availability to work overlapping hours during morning Eastern Time (ET) for team check-ins and coordination. Preferred Qualifications (Good To Have) - Machine Learning & AI: Familiarity with building, tuning, and deploying ML/AI models, rather than simply consuming commercially available model endpoints - Domain Knowledge: Foundational understanding of bioinformatics or biology. - Bioinformatics Data Engineering: Experience within the bioinformatics scope of data engineering, including curating, joining, and munging biological datasets.Python, git lfs, pytorch / huggingface libraries, sklearn Nice to have: CI/CD, containerization, kubeflow, ray pipelines, sksurv, web development (frontend / backend)