AI Scientist

OVERVIEW

Read the full description before applying.

MUST HAVE: Demonstrated experience building LLM-based or agentic AI systems, including tool use, retrieval, structured workflows, and rigorous evaluation. Experience with biomedical or clinical data is strongly preferred.

At M31 Biomedical AI, we are developing foundation models and intelligent systems that can reason across complex biomedical and clinical data. Building on our work in medical imaging and multimodal AI, we are expanding into agentic AI: systems that can retrieve evidence, use tools, coordinate multi-step workflows, and support scientific and clinical reasoning.

We’re seeking a full-time AI Scientist to design, build, and evaluate reliable AI agents for biomedical research and healthcare applications. You’ll work with AI researchers, clinicians, engineers, and research partners to develop systems that can operate across structured and unstructured clinical information while remaining reproducible, auditable, and scientifically grounded.

This position is ideal for someone who has moved beyond prompt engineering and has hands-on experience building agentic systems that solve real tasks.

What You'll Do

· Design and implement agentic AI systems for biomedical and clinical research, including multi-step reasoning, planning, tool use, retrieval, and workflow orchestration

· Develop agents that interact with structured and unstructured data sources such as EHR-derived tables, laboratory data, clinical notes, radiology/pathology reports, and medical imaging metadata

· Build and evaluate retrieval-augmented generation (RAG), structured-output, and tool-calling pipelines with appropriate grounding and provenance

· Develop single-agent and multi-agent workflows where scientifically justified, and compare them against simpler baselines

· Create rigorous evaluation frameworks for task success, factuality, calibration, robustness, reproducibility, latency, and failure modes

· Implement safeguards for high-stakes biomedical settings, including constrained tool access, traceability, human review, and error analysis

· Integrate LLMs and foundation models with APIs, databases, search/retrieval systems, and internal research tools

· Collaborate closely with clinicians, data scientists, and engineers to translate research questions into technically sound agent workflows

· Run controlled experiments and ablations to determine when agentic approaches add value over conventional ML, retrieval, or deterministic pipelines

· Document and maintain reproducible workflows using Git, Python, containers, and cloud-based tools

· Contribute to publications, internal reports, technical documentation, and presentations

Why Join Us

· Be part of a biomedical AI organization developing foundation models and advanced computational methods for healthcare and life sciences

· Collaborate with leading academic, hospital, and technology research teams on high-impact biomedical research

· Work on technically ambitious problems grounded in real scientific and clinical needs

· Contribute to reproducible research with opportunities for publications, presentations, and recognition

• · Work in a mission-driven environment focused on translating computational advances into better biomedical understanding and patient impact

Required Skills & Background

· Master’s or PhD (or equivalent demonstrated research/engineering experience) in Computer Science, Machine Learning, Biomedical Engineering, Computational Biology, or a related field

· Strong Python programming and familiarity with deep learning frameworks (e.g., PyTorch, MONAI, Transformers)

· Hands-on experience building LLM or agentic systems using modern model APIs and/or open-source models

· Demonstrated experience with several of the following: tool/function calling; RAG and vector/semantic retrieval; agent orchestration; structured outputs; long-context systems; memory/state management; model evaluation; prompt/program optimization

· Experience designing quantitative evaluations rather than relying only on qualitative demonstrations

· Understanding of at least one of the following domains:

• Medical Imaging (MRI, CT, Pathology, etc.)
• Genomics or Transcriptomics
• Multi-modal data integration or representation learning

· Experience with data management, reproducibility, testing, and collaborative code development

· Excellent problem-solving, scientific communication, and teamwork skills

Nice-To-Have

· Experience with biomedical, clinical, or life-science data, especially EHR or multimodal patient data

· Experience with clinical NLP, medical reasoning, medical imaging, or biomedical foundation models

· Experience with agent frameworks or orchestration libraries such as LangGraph, AutoGen, Semantic Kernel, or equivalent systems

· Experience with model serving, inference optimization, containers, cloud/HPC environments, or distributed systems

· Experience with privacy-preserving workflows, healthcare data governance, or human-in-the-loop evaluation

· Strong publication record in AI/ML, NLP, agents, biomedical AI, or related areas

· Experience with agentic coding tools such as Claude Code or Codex

Applications Requirements

· Resume/CV

· Cover letter describing a specific agentic or LLM system you built, your role in it, how you evaluated it, and why you are interested in biomedical agentic AI

· GitHub portfolio, publications, preprints, or technical project links (strongly encouraged)

About M31

M31 Biomedical AI is a biomedical imaging company developing foundation models for medical image segmentation and analysis. Our technology enables universal understanding of medical images across modalities and institutions.We’re now collaborating with leading research partners to extend this vision beyond imaging – integrating multi-omics data and intelligent systems that can support biomedical discovery and patient-centered research.

• Job Type: Full-time (12-month renewable contract)
• Location: Hybrid remote – Toronto, ON (M5S 1A8)
• Compensation: CA$65.00 – $90.00/hour, based on experience

Benefits:

• Flexible schedule
• Work-from-home option
• Mentorship and publication opportunities