LLM Engineer, CoLab Software
Offers stock options
Collaboration tech for mechanical design
Be an early applicant
Collaboration tech for mechanical design
101-200 employees
Be an early applicant
Offers stock options
101-200 employees
Company mission
To empower engineering teams to build the future faster.
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
Our take
The mechanical design process requires lots of back-and-forth between engineers, designers and business stakeholders who are giving and acting on feedback. Getting everybody on the same page is time-consuming, and comes with lots of admin and flitting between different platforms, whilst important information slips between the cracks.
This is why CoLab has built a cloud-based platform for collaborative mechanical design and reviewing. It integrates into the existing stack of design and engineering teams to bring together all the teams involved in early-stage design. Basically, the platform pools data, feedback, tracking issues, emails, spreadsheets and notebooks into one collaborative space.
Ultimately, CoLab is a simple way to collaborate on concepts and projects at the planning stage, thus nipping in the bud any problems that could arise down the line. With an impressive series of investments behind it, the company plans to scale up its market push, explore the possibilities of AI and expand its team.
Steph
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 6 rounds)
Mar 2025
$4m
LATE VC
May 2024
$21m
SERIES B
Total funding: $45.6m
Company 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
Company HQ
East End, St. John's, NL
Leadership
Jeremy Andrews
(Co-Founder & CTO)Previosuly Mechanical Engineering Advisor at Paradigm Hyperloop, Team Lead at Memorial Baja, Battery Enclosure Engineering at Tesla, and Systems Test Engineer at General Dynamics Mission Systems.
Adam Keating
(Co-Founder & CEO)Currently Co-Founder and Strategic Advisor at DuXion. Previously Project Lead at Paradigm Hyperloop, Mechanical Engineer at RefleXion Medical and Production Engineer at Statoil.
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