Artificial Intelligence Engineer

Bios Life is a technology-enabled healthcare company built to empower individuals to proactively manage their cancer risk, redefining personalized cancer screening and survivorship.Surveillance for people at high risk of cancer and for cancer survivors is too often generic, fragmented, and reactive. Screening follows population averages rather than individual risk. Data, expertise, and care delivery sit apart from one another, and care tends to wait for a problem to appear before responding.Bios Life is building the operating system for personalized cancer surveillance: a platform that reads genomic, clinical, laboratory, and lifestyle data together, models how an individual’s risk changes over time, and integrates that intelligence with a continuously curated evidence base – all delivered by a clinician-led care team specialized in cancer surveillance.We offer two programs:Personalized Screening — an integrated pan-cancer screening program built around an individual’s risk profile.Survivorship — continuous surveillance and holistic care designed around the specific needs of cancer survivors.Our team pairs decades of experience at the forefront of innovative cancer care with deep expertise in genomics and machine learning, supported by internationally recognized advisors who have helped shape screening and survivorship guidelines.The RoleAs an AI Engineer, you will build the machine learning infrastructure and models that transform complex biomedical data into actionable clinical insights. You will take research concepts from prototype through production, developing AI systems that support cancer biomarker detection, risk prediction, longitudinal monitoring, and personalized screening strategies.You will report to the Head of AI and collaborate closely with clinicians, data scientists, product leaders, and software engineers. This is an in-person role based in our Boston office, with the opportunity to shape both our technology and engineering culture from the ground up.What You'll DoMachine Learning Development & DeploymentDesign, develop, train, evaluate, and deploy machine learning models for:Cancer biomarker detectionRisk stratificationLongitudinal patient monitoringPersonalized screening and survivorship recommendationsTranslate research models into reliable, production-ready systemsDevelop and optimize deep learning architectures, including foundation models and multimodal learning approachesML Infrastructure & EngineeringBuild scalable machine learning pipelines covering:Data ingestionFeature engineeringModel trainingModel servingPerformance monitoringDevelop cloud-based ML infrastructure using platforms such as AWS or GCPLeverage GPU/accelerated computing environments for large-scale model developmentImplement strong engineering practices including:Version controlTestingCI/CDContainerized deployments (Docker)Biomedical Data & Clinical AIPartner with clinical and data teams to curate, validate, and manage complex datasets from sources including:GenomicsMedical imagingElectronic health records (EHR)Other multimodal healthcare data sourcesBuild evaluation frameworks to ensure model performance, reliability, and clinical relevanceCollaborate with clinicians and scientific stakeholders to align AI capabilities with real-world healthcare workflowsResearch Translation & InnovationBridge cutting-edge AI research with practical clinical applicationsSupport development and refinement of AI systems as the platform evolvesContribute to technical strategy and architecture decisions as an early engineering team memberWhat You BringRequired QualificationsMS or PhD in Computer Science, Machine Learning, Computational Biology, Bioinformatics, or a related quantitative field3+ years of hands-on experience building and deploying machine learning models in production environmentsStrong programming skills in PythonExperience with modern machine learning frameworks such as:PyTorchTensorFlowJAXExperience building ML pipelines and deploying models in cloud environments (AWS, GCP, or similar)Experience with containerized development environments (Docker)Strong software engineering fundamentals:Git/version controlTestingCI/CD workflowsCode review practicesAbility to communicate complex technical concepts clearly with both technical and non-technical stakeholdersPreferred QualificationsExperience working with healthcare or biomedical datasets, including:GenomicsPathologyImagingEHR dataExperience developing AI systems in healthcare, biotech, or life sciences environmentsPublications, research contributions, or open-source work in:Machine learningComputational biologyHealthcare AIExperience working in an early-stage startup environmentEntrepreneurial mindset with a willingness to take ownership and solve ambiguous problems