Role Overview

You’ll fine-tine, research, and train AI models specific to lifescience and diagnostics. You’ll work on R&D projects that will have meaningful impact on how our customers capture data from instruments. You’ll automate data pipelines for lab results using AI and orchestration. Ultimately, this will feed into a memory layer that suggests scientists what transformations to use based on their assay type and instruments.

The project will directly feed into a meaningful AI full stack app for all diagnostic instruments on this planet. It will help scientists to get to sample results 70% faster and reduce 50% error rate in the process.

‍

Key Responsibilities

• Develop machine-learning models for experiment data.
• Build Python services and APIs.
• Integrate AI tools with lab workflows.
• Collaborate with scientists to refine solutions.
• Write clean, tested code.

Must-Have Qualifications

• Bachelor’s or Master’s in CS, Engineering, Data Science, or related field.
• 0–3 years of hands-on AI/ML experience.
• Proficient in Python and popular ML libraries (e.g., PyTorch, scikit-learn).
• Experience with API development (FastAPI, Flask, or similar).
• Strong problem-solving skills and attention to detail.
• Legal right to work in Canada (valid work permit or protected status).
• Recent graduate or early-career candidate.

Nice-to-Have

• Familiarity with cloud platforms (AWS EKS, S3).
• Exposure to lab data (CSV, JSON, instrument files).
• Experience with CI/CD and containerization (Docker, Kubernetes).
• Knowledge of natural-language processing or computer vision.

What We Offer

• Competitive salary
• Flexible hours and hybrid work.
• Mentorship from experienced AI and biotech experts.
• Access to Communitech and Velocity programs.
• Health benefits and generous stock options.
• Budget for training

Your Two Year Roadmap

Month 1-6, you will:

• Enhance Recommendation AI
  • Use prompt engineering and AI pipelines with LLMs for better suggestions.
  • Aim for performance and scalability.
• Scale API and GLUE Layer
  • Build strong ETL support for enterprise loads.
  • Build SDK framework for Scispot APIs
• Introduce NLP for Instrument Integration
  • Offer script templates so scientists can process data easily.
• Suggest Telemetry Improvements
  • Improve monitoring for infrastructure health.
• Graphical Chain of Custody
  • Let users query sample journeys with prompts using graph database

Month 7-12, you will:

• EKS Migration
  • Grow & Maintain AWS EKS cluster
• Automated Testing
  • Increase backend unit test coverage.
• MCP Layer for Recommendation
  • Allow AI agents to take simple actions for scientists.
• Upgrade Search
  • Improve OpenSearch and vector databases.
• Memory Layer for Agents
  • Reduce reliance on retrieval-augmented generation by building memory layer for AI agents

Month 13-24, you will:

• Lead Core Application Team
  • Oversee tech vision, architecture, and development.
• App Store for Instrument Connectors
  • Expose our instrument integrations in a user-friendly marketplace.

Why You Might Love This Role

• You want to shape the future of scientific research.
• You enjoy solving complex AI challenges.
• You like leading from the front, mentoring, and guiding teams.
• A chance to build next-gen AI tools for lab workflows.
• Leadership role with a high level of autonomy.

Why You Might Not

• You dislike fast-paced startup environments.
• You prefer strictly defined roles.

‍