Software Development Engineer, Health & Wellness, Health Tech

This is a unique opportunity to join a small, high-impact team applying machine learning and bioinformatics to health initiatives. You will lead the architecture and implementation of the ML systems that take genomic, proteomic, and clinical signal from raw biology to actionable predictions — and you'll do it on a team where engineering decisions directly shape scientific outcomes. If you're passionate about building robust, scalable systems for ML in the life sciences — high-throughput pipelines for sequencing and multi-omics data, low-latency inference services for biological foundation models, and the surrounding tooling that makes model iteration fast and reproducible — this role is for you. You'll design and implement high-performance APIs and data pipelines, integrate ML models into production environments, and ensure the reliability and efficiency of our bioinformatics workflows. You'll work elbow-to-elbow with ML researchers, computational biologists, and immunologists to turn experimental ideas into deployed systems. We're especially looking for engineers who want to dive into immunology — neoantigen prediction, MHC binding and presentation, T-cell response modeling, and the broader machinery of the adaptive immune system. You don't need to come in as an immunologist, but you should be excited to learn the biology deeply enough to make sound architectural choices: how to represent peptides and structures, where biological priors belong in the system, and how to design feedback loops between wet-lab data and model improvement. In this early-stage initiative, you'll have significant influence over technical direction, engineering practices, and the ML/bioinformatics stack itself. This is an excellent opportunity for a high-judgment engineer to demonstrate impact, make key architectural decisions, and build systems where the bar for correctness is set by biology — not just uptime. Key job responsibilities - Lead software architecture design for ML and bioinformatics systems that integrate genomic, proteomic, and clinical data. - Develop high-performance APIs for model inference and pipeline orchestration across sequencing, multi-omics, and immunology workloads. - Implement integration of ML models — including biological foundation models and structure-aware predictors — into prototype and production applications. - Ensure efficient integration of selected models for tasks such as neoantigen prediction, MHC binding/presentation, and peptide ranking. - Optimize system performance, scalability, and reliability for high-throughput bioinformatics pipelines and low-latency inference services.