Principal Engineer - AI & Full Stack
Principal Engineer - AI & Full Stack at Scispot — ON, CA, US
- Company: Scispot
- Location: ON, CA, US
- Employment type: FULL_TIME
- Salary: USD 120000–175000 / year
- Posted: 2026-07-17
About this role
Scispot is building the digital backbone for scientific discovery. We empower biotech teams by unifying lab operations, data flow, and AI-driven insights.
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.