Scispot is building the digital backbone for scientific discovery. We empower biotech teams by unifying lab operations, data flow, and AI-driven insights.
We are hiring a Principal Engineer – AI and Full Stack to lead an R&D scientific innovation project aimed at advancing Scispot’s AI-powered Lab Operating System. The role will focus on designing, prototyping, and commercializing new features that integrate AI, data science, and full-stack engineering to improve biotech lab workflows.
Key Activities
Lead research and development of AI-enabled modules for data capture, analysis, and visualization in life science labs.
Prototype and test new product features with pilot customers to validate technical feasibility and market fit.
Conduct technical and market analysis to optimize adoption of AI-driven lab tools.
Build and refine full-stack architectures that integrate with lab instruments, APIs, and cloud-based platforms.
Deliver measurable outcomes, such as reducing manual data entry time and increasing workflow automation in pilot labs.
Outcomes & Impact
Creation of innovative, market-ready tools that enhance lab efficiency and data quality.
Contribute to commercialization efforts through pilot deployments and adoption in Canadian and international labs.
Role Overview
You will own our AI and full-stack engineering efforts
You will shape next generation features that help scientists run experiments faster
You will guide our platform's scalability and drive new integrations for lab instruments
How will you spend your time?
50% coding and system design (React, Python, Java + AI integration)
20% product iteration and user feedback loops
10% collaboration, planning, and roadmap refinement
10% data engineering, infrastructure and embedding strategies
10% LLM experimentation (prompting, AI pipelines, graph DBs, vector DBs)
What You’ll Do
Architect and Scale
Build robust backend services with intuitive UI/UX (React, Java Spring Boot, AWS, Kubernetes).
Develop new AI-based features for enterprise customers.
Elevate Our AI Stack
Enhance recommendation engines with prompt engineering and LLMs. Building AI pipelines with LLMs.
Introduce NLP for seamless instrument integration.
Drive Quality and Automation
Implement automated tests.
Oversee telemetry improvements.
Lead and Mentor
Collaborate with product, data, and design teams.
Grow a team of engineers focused on cutting-edge AI tools.
Required Skills
Proficiency in Java, Python, React & Javacript
Experience deploying to AWS (EKS, Lambda, or EC2).
Deep knowledge of AI pipelines, LLMs, and NLP libraries.
Familiarity with data stores (OpenSearch, vector databases, graph databases).
Strong leadership and communication skills.
Bonus Skills
Experience with scientific or biotech workflows.
Knowledge of advanced ETL, data streaming, or prompt engineering.
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.
Tech Stack:
Frontend: React JS and Typescript
Backend: Elastic Search, AWS Lambda, Rabbit MQ, Mongo DB, S