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, S3, Java Spring Boot
- Architecture: Microservices integrated with GraphQL and Rest APIs
- AI Infrastructure: TensorFlow (Proprietary ML), Azure AI Service, Azure Open AI service, AI Pipelines, Programmatic Prompt Engineering Ideal Candidate Profile:
- Proficient with AWS and its suite of data services.
- Hands-on experience with tools such as Lambda function, MQ, Java spring boot, Elastic Search, Python, Mongo DB, Dynamo DB, and S3 bucket.
- Strong programming skills, particularly in Python, Java, React & Javascript.
- Good understanding of different Agentic AI architectures.
- Good understanding of learning how to build AI pipelines with LLMs.
- A solid grasp of microservices and associated best practices.
- Experience in data engineering and orchestration is preferred.
- Loves working in a fast paced startup environment. Why Join Scispot?:
- Work from anywhere but ideally based out of Canada.
- Engage in challenging, impactful work in the realm of biotech data and AI.
- Competitive stock options.
- Unlimited growth upside. 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.
Application
If shaping the future of biotech data infrastructure resonates with you, we'd like to hear more. Send your resume to satya@scispot.io.