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.