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<html><body>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. o 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. o Verify Model Integrity: Verify that imported models are working properly and consistently generating expected outputs. &nbsp;• Maintain Platform Codebase: Maintain the codebase for the internal foundation model benchmarking platform across three dimensions: data, models, and prediction tasks. • Execute Data Engineering: Perform data engineering tasks, including pulling from existing curated data stores, executing additional filtering, 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 • Support 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. &nbsp;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 &nbsp;• 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. &nbsp; • 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 &amp; 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.</body></html>

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. o 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. o Verify Model Integrity: Verify that imported models are working properly and consistently generating expected outputs. &nbsp;• Maintain Platform Codebase: Maintain the codebase for the internal foundation model benchmarking platform across three dimensions: data, models, and prediction tasks. • Execute Data Engineering: Perform data engineering tasks, including pulling from existing curated data stores, executing additional filtering, 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 • Support 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. &nbsp;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 &nbsp;• 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. &nbsp; • 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 &amp; 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.

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