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.
As an LLM Engineer at CoLab, you’ll work closely with our ML team to support model development, deployment, and maintenance. You’ll help with data prep, pipeline updates, and implementation tasks that form the backbone of our AI-powered features.
This is a hands-on, execution-focused role, ideal for someone early in their ML career. You’ll be expected to learn fast, work through ambiguity, and bring care and consistency to the details. Over time, you’ll gain exposure to the full ML lifecycle—from building datasets to deploying production models—and grow your skills alongside experienced engineers.
If you're excited about AI/ML, want to contribute to a real product, and are ready to do the foundational work required to make ML systems run reliably at scale, we’d love to hear from you.
About The RoleYou’ve got a basic understanding of ML and NLP concepts, can write python code confidently, and are eager to get your hands dirty. You're looking for a role where you can grow your skills by doing real work—not just reading research papers.
You’re detail-oriented, resourceful, and take pride in getting things right the first time—even if the work is unglamorous. You don’t wait to be told what to do; you take initiative, ask good questions, and always follow through.
This role isn’t for someone looking to focus purely on research or theoretical ML. It’s for someone who wants to learn by building and supporting the systems that make AI work in the real world.
Job Responsibilities- Support ML model development by preparing datasets, writing scripts, and maintaining training pipelines
- Help implement and test model features, experiments, and infrastructure updates
- Contribute to code that integrates ML models into our production environment
- Assist in building evaluation pipelines and observability dashboards for ensuring and maintaining the quality of our ML models going to production.
- Monitor model behavior in production and help troubleshoot performance issue
- Assist with setting up automated testing, logging, and reporting
- Work with the ML, Architecture, and platform teams to keep infrastructure clean, reproducible, and scalable
- Write documentation and contribute to reusable components that improve team efficiency
- Stay curious—ask questions, share what you learn, and actively look for ways to improve tools or processes
- Bachelor's degree (or close to completing one) in Computer Science, Engineering, Data Science, or a related field
- Familiarity with Python and ML libraries like Scikit-Learn, Hugging Face, or PyTorch
- Exposure to NLP or AI concepts (e.g., transformer architecture, embeddings, LLMs)—even via personal projects or coursework
- General familiarity with LLM optimization techniques, including zero shot, one shot and few shot prompt engineering, RAG, fine tuning, CoT, ReACT, etc.
- Some experience with databases and writing queries (SQL or vector DBs like Pinecone, OpenSearch is a plus)
- Interest in cloud tools (e.g., AWS, GCP, Azure) and ML services in the cloud infrastructure
- Experience with Git or version control workflows
- Comfortable working independently and taking ownership of tasks with guidance
- Strong written and verbal communication skills
- A growth mindset—you’re open to feedback and eager to improve every day.
- Compensation: This is a full-time, permanent position with a competitive compensation package that includes a stock options package
- Benefits: This role offers an extended health and benefits package that includes unlimited paid vacation and RRSP matching
- Remote/Hybrid Work: Our main office is in St. John’s, NL, where we offer hybrid and remote opportunities. This role has the flexibility to work from anywhere within Canada.
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.
