Product Engineer - AI (R&D)

Job description

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.

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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.