About CoLab
At CoLab, we want to help mechanical engineering teams bring life-changing products to market years sooner.
CoLab is a cloud based platform for engineering design review. We make it easy for subject matter experts (SMEs) across your business to access, evaluate, and comment on 2D drawings and 3D models. Our built-in AI peer checker, AutoReview, scans designs for common errors or non-compliance with your standards and guidelines. AutoReview creates markups and comments on your files, in context – just like a human checker.
With CoLab, human SMEs and AI work together to help you make better decisions and improve designs faster. We automatically capture knowledge from across your global business that would otherwise be buried in emails, spreadsheets, slide decks, and unknown locations in Sharepoint or PLM. Then, we make sure every lesson learned and every design guideline is applied exactly when it matters.
Companies like Johnson Controls, Komatsu, Schaeffler, and Polaris have launched products 40% faster, cut BOM costs by 50%, and reduced quality escapes by 15% in 1 year.
About the Role
This isn’t a typical full stack role. You’ll work at the intersection of high-scale software and real-world AI applications—building features and infrastructure that integrate deeply with large language models (LLMs) and ML systems.
You’ll be part of the Build Scale team and help power critical product experiences like AutoReview—our AI-assisted design checker—and future LLM capabilities that help engineers make better decisions, faster. You’ll own end-to-end delivery: from building the backend APIs and vector search pipelines, to shaping and optimizing the frontend UI that engineers rely on every day.
This is a role for someone who wants to do the hard thing: ship real LLM and ML-powered features in a production SaaS product used by global engineering teams. If you’re comfortable taking ownership, care about the full stack, and are energized by cutting-edge AI work, you’ll thrive here.
Our Ideal Candidate
You’ve built and maintained scalable full-stack applications before, ideally in a SaaS or distributed systems environment. You bring deep experience in both backend (Python, APIs, data pipelines) and frontend (React, TypeScript, UX patterns) development.
In addition to your core developer skills, you’ve worked hands-on with LLMs and ML tooling. You’re not afraid to experiment with new tools, write your own prompts, and optimize performance across the stack.
You’re a pragmatic builder. You keep things simple. You collaborate well across disciplines. And you know when to ship and when to polish.
Responsibilities
- Build and maintain user-facing features and APIs that integrate LLM and ML capabilities
- Design and scale backend systems to support vector search, agent workflows, and human-AI collaboration
- Collaborate closely with product and design to shape intelligent, intuitive user experiences
- Prototype, prompt, and evaluate LLM solutions using tools like OpenAI, Anthropic, and Hugging Face
- Mentor teammates through code reviews, design sessions, and paired debugging
- Contribute to technical direction and best practices across the stack
- Measure and optimize performance and reliability at scale
Qualifications
- 5+ years full-stack development experience
- Proficient in Python and its libraries, with hands-on experience in designing, developing, and maintaining robust and scalable APIs.
- Proficient in React, TypeScript and front-end tools such as Webpack, Vite, npm and ESLint
- Hands-on experience with relational databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g.: DynamoDB), vector databases (e.g., OpenSearch, Pinecone, FAISS) and querying frameworks/APIs (e.g.: SQLAlchemy).
- Experience writing robust automated tests, including unit, as well as end-to-end (E2E) tests using Cypress or Playwright.
- AI/ML Experience:
- Libraries: PyTorch, Hugging Face, Scikit-Learn
- Agent workflows: pydantic-ai
- Experience integrating and prompting large language models (LLMs) via APIs (e.g., OpenAI, Anthropic, Google Gemini)
- Experience working with ML services in the cloud infrastructure, e.g. Sagemaker, Bedrock, etc.
- Solid Git workflow knowledge and test-driven development practices
- Self-directed, curious, and collaborative
- GraphQL experience
- Familiarity with observability tools (e.g., DataDog, Retool)
Extra Details
- Compensation: Competitive salary with stock options
- Benefits: Extended health and dental, RRSP matching, unlimited paid vacation
- Work Policy: Remote within Canada
Equity Note
Frequently cited statistics show that people who identify with historically marginalized groups are likely to apply to jobs only if they meet 100% of the qualifications. We encourage you to help us break that statistic and apply even if you don’t meet every single qualification—your potential is what matters most to us.
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