Top Benefits
About the role
Who you are
- This is a hands-on, execution-focused role, ideal for someone early in their ML career
- 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
- You’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
- It’s for someone who wants to learn by building and supporting the systems that make AI work in the real world
- 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
What the job involves
- 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
- 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
- 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
Benefits
- Health and dental insurance (covered at 100% for the employee)
- Unlimited PTO
- Remote/Hybrid Work: Our main office location is in St. John's, NL where we offer hybrid and remote opportunities
About CoLab Software
Your most critical product decisions are made everyday in design reviews.
But as products get more complex and teams become larger and more specialized, the design review process looks the same as it did 10-20 years ago.
So what happens?
-43% of design feedback is never documented or addressed -87% of engineering leaders say it takes hours or days to trace the rationale behind a single design decision -90% of companies have product launch delays due to late-stage design changes.
These problems won’t be solved by more meetings, better PLM workflows or increased pressure to keep slide decks and spreadsheets up-to-date. These are problems that require a completely new way for engineering teams to work together.
CoLab is a cloud based platform purpose built for fast, effective design review. Using CoLab, multiple engineers, designers, and other stakeholders can review designs together and build off one another's feedback.
CoLab makes it easy to review the right data (including CAD) with all the right people, capture useful feedback, and track issues through to action. CoLab pulls together design discussions previously lost in emails, spreadsheets, and notebooks into a single platform that integrates back into PLM. We call it a Design Engagement System.
Top Benefits
About the role
Who you are
- This is a hands-on, execution-focused role, ideal for someone early in their ML career
- 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
- You’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
- It’s for someone who wants to learn by building and supporting the systems that make AI work in the real world
- 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
What the job involves
- 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
- 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
- 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
Benefits
- Health and dental insurance (covered at 100% for the employee)
- Unlimited PTO
- Remote/Hybrid Work: Our main office location is in St. John's, NL where we offer hybrid and remote opportunities
About CoLab Software
Your most critical product decisions are made everyday in design reviews.
But as products get more complex and teams become larger and more specialized, the design review process looks the same as it did 10-20 years ago.
So what happens?
-43% of design feedback is never documented or addressed -87% of engineering leaders say it takes hours or days to trace the rationale behind a single design decision -90% of companies have product launch delays due to late-stage design changes.
These problems won’t be solved by more meetings, better PLM workflows or increased pressure to keep slide decks and spreadsheets up-to-date. These are problems that require a completely new way for engineering teams to work together.
CoLab is a cloud based platform purpose built for fast, effective design review. Using CoLab, multiple engineers, designers, and other stakeholders can review designs together and build off one another's feedback.
CoLab makes it easy to review the right data (including CAD) with all the right people, capture useful feedback, and track issues through to action. CoLab pulls together design discussions previously lost in emails, spreadsheets, and notebooks into a single platform that integrates back into PLM. We call it a Design Engagement System.