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Confirmed on the employer's own hiring board on Nov 11, 2026. First seen by Alion on Oct 22, 2026.

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Personalized cancer surveillance for high-risk individuals and survivors — proactive screening and continuous care, powered by AI and grounded in human care.

About BIOS Life

BIOS Life is building the world’s first AI-powered personalized cancer surveillance platform, combining proprietary models, unique datasets, and advanced diagnostics to provide proactive monitoring and peace of mind for cancer survivors and high-risk individuals at scale.

Our founding AI team brings deep experience training foundation models on genomic data and is now combining these capabilities with EHR foundation models to develop a new class of cancer screening models and reinforcement-learning recommendation systems. Our mission is to ensure foundational AI research translates into meaningful clinical value from day one.

The Role

As an AI Scientist at BIOS Life, you will drive the research and development of the machine-learning systems powering our personalized cancer surveillance platform. Your work will span foundation models, cancer risk prediction, biomarker detection, longitudinal monitoring, and reinforcement-learning systems designed to recommend optimal screening and follow-up strategies.

You will help keep BIOS Life at the forefront of AI, healthcare, and biology, shape our research roadmap, and translate scientific advances into practical clinical applications.

Reporting to the Head of AI, you will work closely with our clinical, product, and engineering teams. As an early-stage hire, you will have significant influence over our scientific direction, technical approach, and research culture.

Key Responsibilities

Research Strategy & Scientific Innovation

  • Survey the AI, genomics, and oncology literature to identify emerging methods and high-impact research opportunities.

  • Help shape the research roadmap and advance the state of the art in AI-driven cancer surveillance.

  • Translate scientific questions and clinical needs into actionable research priorities.

Machine Learning Model Development

  • Design, train, and evaluate models for cancer biomarker detection, risk stratification, and longitudinal monitoring.

  • Develop and investigate reinforcement-learning approaches to recommend personalized screening and survivorship strategies.

  • Fine-tune and adapt foundation models to genomic, electronic health record (EHR), and other relevant biomedical data.

Research-to-Production

  • Partner with engineering to translate research prototypes into scalable, reliable production systems.

  • Bridge multimodal learning and clinical workflows, ensuring research outputs can support real-world applications.

  • Contribute to the development of modern AI pipelines and infrastructure.

Scientific Evaluation & Rigor

  • Design rigorous experimental frameworks and benchmarking strategies to evaluate model performance.

  • Develop and contribute to clinical validation protocols, with appropriate attention to generalizability, robustness, and limitations.

  • Maintain high standards of scientific integrity, reproducibility, and technical quality.

Publications & Scientific Communication

  • Lead and contribute to peer-reviewed publications, conference papers, and technical reports.

  • Communicate complex technical findings clearly to scientific, clinical, and cross-functional audiences.

  • Represent BIOS Life in scientific forums and collaborations with academic and clinical partners.

Cross-Functional Collaboration

  • Work closely with oncologists, epidemiologists, product managers, and engineers to align research with clinical needs and product objectives.

  • Collaborate across disciplines to turn promising scientific advances into meaningful improvements in cancer surveillance.

What You Bring

Required Qualifications

  • PhD in Machine Learning, Computer Science, Computational Biology, Bioinformatics, or a related quantitative field.

  • Strong publication record in applied AI, including large-scale machine learning or foundation models, and/or research at the intersection of AI, biology, and healthcare.

  • Hands-on experience training and evaluating machine-learning models.

  • Proficiency in Python and modern ML frameworks such as PyTorch, JAX, or TensorFlow.

  • Familiarity with cloud-based GPU/TPU computing and modern machine-learning pipelines.

  • Solid software engineering fundamentals, including version control, testing, and code review.

  • Excellent scientific writing and communication skills, with the ability to explain complex concepts to technical and clinical audiences.

Preferred Qualifications

  • Experience working with biomedical or healthcare data, such as genomics, pathology, or EHRs.

  • Track record of translating research into real-world clinical applications or production products.

  • Experience with foundation models, multimodal learning, reinforcement learning, or personalized risk prediction.

  • Entrepreneurial mindset and ability to thrive in a fast-moving, mission-driven startup.

  • Previous experience at an early-stage startup or building machine-learning systems from zero to one.

What We Offer

  • The opportunity to work alongside founders and executives shaping our AI platform.

  • Meaningful equity participation and the opportunity to share in the company’s success.

  • Competitive compensation and comprehensive benefits.

  • A collaborative, in-person working environment at our Boston office, four days per week.

  • Access to world-class datasets and clinical collaborators.

  • A mission-driven opportunity to improve outcomes for cancer survivors and high-risk individuals.

  • A culture of scientific rigor, intellectual curiosity, and individual ownership.

Join Us

At BIOS Life, you will help advance AI research with the potential to change how cancer survivors and high-risk individuals are monitored and supported. If you are excited by challenging scientific problems and motivated to translate cutting-edge machine learning into meaningful clinical impact, we would love to hear from you.

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