AI Engineer

About Bios Life

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.

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.

About the Team

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.

Responsibilities

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

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