{"id":3995427,"url":"https://alion.io/job/bios-life-ai-engineer","title":"AI Engineer","company":{"id":4478736,"name":"Bios Life","domain":"bioslife.ai","url":"https://alion.io/company/bios-life","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cambridge, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-10-05T17:21:11Z","employer_posted_date":"2026-11-09","last_verified_at":"2026-11-10T01:05:37Z","board_verified":true,"closed_at":null,"days_open":35,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":35},"description":"Bios Life is a technology-enabled healthcare company built to empower individuals to proactively manage their cancer risk, redefining personalized cancer screening and survivorship.\nSurveillance 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.\nBios 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.\nWe offer two programs:\nPersonalized Screening - an integrated pan-cancer screening program built around an individual’s risk profile.\nSurvivorship - continuous surveillance and holistic care designed around the specific needs of cancer survivors.\nOur 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.\nThe Role\nAs 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.\nYou 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.\nWhat You'll Do\nMachine Learning Development & Deployment\nDesign, develop, train, evaluate, and deploy machine learning models for:\nCancer biomarker detection\nRisk stratification\nLongitudinal patient monitoring\nPersonalized screening and survivorship recommendations\nTranslate research models into reliable, production-ready systems\nDevelop and optimize deep learning architectures, including foundation models and multimodal learning approaches\nML Infrastructure & Engineering\nBuild scalable machine learning pipelines covering:\nData ingestion\nFeature engineering\nModel training\nModel serving\nPerformance monitoring\nDevelop cloud-based ML infrastructure using platforms such as AWS or GCP\nLeverage GPU/accelerated computing environments for large-scale model development\nImplement strong engineering practices including:\nVersion control\nTesting\nCI/CD\nContainerized deployments (Docker)\nBiomedical Data & Clinical AI\nPartner with clinical and data teams to curate, validate, and manage complex datasets from sources including:\nGenomics\nMedical imaging\nElectronic health records (EHR)\nOther multimodal healthcare data sources\nBuild evaluation frameworks to ensure model performance, reliability, and clinical relevance\nCollaborate with clinicians and scientific stakeholders to align AI capabilities with real-world healthcare workflows\nResearch Translation & Innovation\nBridge cutting-edge AI research with practical clinical applications\nSupport development and refinement of AI systems as the platform evolves\nContribute to technical strategy and architecture decisions as an early engineering team member\nWhat You Bring\nRequired Qualifications\nMS or PhD in Computer Science, Machine Learning, Computational Biology, Bioinformatics, or a related quantitative field\n3+ years of hands-on experience building and deploying machine learning models in production environments\nStrong programming skills in Python\nExperience with modern machine learning frameworks such as:\nPyTorch\nTensorFlow\nJAX\nExperience building ML pipelines and deploying models in cloud environments (AWS, GCP, or similar)\nExperience with containerized development environments (Docker)\nStrong software engineering fundamentals:\nGit/version control\nTesting\nCI/CD workflows\nCode review practices\nAbility to communicate complex technical concepts clearly with both technical and non-technical stakeholders\nPreferred Qualifications\nExperience working with healthcare or biomedical datasets, including:\nGenomics\nPathology\nImaging\nEHR data\nExperience developing AI systems in healthcare, biotech, or life sciences environments\nPublications, research contributions, or open-source work in:\nMachine learning\nComputational biology\nHealthcare AI\nExperience working in an early-stage startup environment\nEntrepreneurial mindset with a willingness to take ownership and solve ambiguous problems","description_format":"text","description_chars":4865,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Precision Medicine","Cancer Care Centers"],"lifecycle":[{"event":"open","at":"2026-11-10T01:05:37Z"}],"visa":[{"country":"US","licensed_sponsor":true,"adverts_offering_sponsorship_24m":0,"sanctioned_now":false,"evidence":"H-1B filings in 12 months: 1","filings_12m":1,"filings_prev_12m":0,"green_card_filings_12m":0,"median_offered_wage_usd":227500,"route":null,"cap_exempt":false,"checked_at":"2026-11-09T07:32:21+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, 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