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 Role
As 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 Do
Machine Learning Development & Deployment
Design, develop, train, evaluate, and deploy machine learning models for:
Cancer biomarker detection
Risk stratification
Longitudinal patient monitoring
Personalized screening and survivorship recommendations
Translate research models into reliable, production-ready systems
Develop and optimize deep learning architectures, including foundation models and multimodal learning approaches
ML Infrastructure & Engineering
Build scalable machine learning pipelines covering:
Data ingestion
Feature engineering
Model training
Model serving
Performance monitoring
Develop cloud-based ML infrastructure using platforms such as AWS or GCP
Leverage GPU/accelerated computing environments for large-scale model development
Implement strong engineering practices including:
Version control
Testing
CI/CD
Containerized deployments (Docker)
Biomedical Data & Clinical AI
Partner with clinical and data teams to curate, validate, and manage complex datasets from sources including:
Genomics
Medical imaging
Electronic health records (EHR)
Other multimodal healthcare data sources
Build evaluation frameworks to ensure model performance, reliability, and clinical relevance
Collaborate with clinicians and scientific stakeholders to align AI capabilities with real-world healthcare workflows
Research Translation & Innovation
Bridge cutting-edge AI research with practical clinical applications
Support development and refinement of AI systems as the platform evolves
Contribute to technical strategy and architecture decisions as an early engineering team member
What You Bring
Required Qualifications
MS or PhD in Computer Science, Machine Learning, Computational Biology, Bioinformatics, or a related quantitative field
3+ years of hands-on experience building and deploying machine learning models in production environments
Strong programming skills in Python
Experience with modern machine learning frameworks such as:
PyTorch
TensorFlow
JAX
Experience 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 control
Testing
CI/CD workflows
Code review practices
Ability to communicate complex technical concepts clearly with both technical and non-technical stakeholders
Preferred Qualifications
Experience working with healthcare or biomedical datasets, including:
Genomics
Pathology
Imaging
EHR data
Experience developing AI systems in healthcare, biotech, or life sciences environments
Publications, research contributions, or open-source work in:
Machine learning
Computational biology
Healthcare AI
Experience working in an early-stage startup environment
Entrepreneurial mindset with a willingness to take ownership and solve ambiguous problems