Asks for

The ad says“Solid grasp of 3D math with practical experience in graphics engines, CAD kernels (HOOPS, OpenCascade, Parasolid), Robotics, 3D simulations, etc.”
The ad says“Familiarity with 3D model ML pipelines (e.g., feature extraction, embedding generation, model training, or inference)”
The ad says“Solid grasp of 3D math with practical experience in graphics engines, CAD kernels (HOOPS, OpenCascade, Parasolid), Robotics, 3D simulations, etc.”
The ad says“Familiarity with agentic workflows (e.g., pydantic-ai)”
The ad says“Experience deploying ML services in cloud infrastructure (e.g., AWS SageMaker, Amazon Bedrock, etc.)”
The ad says“Hands-on experience designing, developing, and maintaining robust, scalable backend systems and APIs”
The ad says“Experience deploying ML services in cloud infrastructure (e.g., AWS SageMaker, Amazon Bedrock, etc.)”
The ad says“5+ years of experience in backend development, ideally in a product or platform team”
The ad says“Solid grasp of 3D math with practical experience in graphics engines, CAD kernels (HOOPS, OpenCascade, Parasolid), Robotics, 3D simulations, etc.”
The ad says“Solid grasp of 3D math with practical experience in graphics engines, CAD kernels (HOOPS, OpenCascade, Parasolid), Robotics, 3D simulations, etc.”
The ad says“Knowledge of ML frameworks such as PyTorch, Hugging Face, or Scikit-learn”
The ad says“Experience integrating and prompting large language models (LLMs) via APIs (e.g., OpenAI, Anthropic, Google Gemini)”
The ad says“Experience with ML/AI/LLM concepts and productionalization”
The ad says“Experience working with multiple data storage paradigms (SQL, NoSQL, and vector databases)”
The ad says“Familiarity with agentic workflows (e.g., pydantic-ai)”
The ad says“Knowledge of ML frameworks such as PyTorch, Hugging Face, or Scikit-learn”
The ad says“Solid grasp of 3D math with practical experience in graphics engines, CAD kernels (HOOPS, OpenCascade, Parasolid), Robotics, 3D simulations, etc.”
The ad says“Knowledge of ML frameworks such as PyTorch, Hugging Face, or Scikit-learn”
The ad says“Experience working with multiple data storage paradigms (SQL, NoSQL, and vector databases)”
The ad says“Experience working with multiple data storage paradigms (SQL, NoSQL, and vector databases)”

About CoLab

At CoLab, we help mechanical engineering teams bring life-changing products to market years sooner.

We build AI for engineers designing cars, medical devices, and jet engines. Products this complex aren't sketched and sent to production. Teams spend months on iterations, reviews, and tests, predicting how every part performs before anything is built. CoLab is where those reviews happen, and we capture the rationale behind every change. Other tools show what changed between versions. Only CoLab knows why. That's how our AI helps manufacturers like Bobcat, Triumph Motorcycles, and GE Appliances make better decisions, eliminate rework cycles, and get to market faster.

Proudly based in St. John’s, Newfoundland, CoLab started with one customer in 2018. Since then we’ve raised a $72M Series C, been named the 57th fastest growing company in North America by Deloitte (Fast 500™), signed multi-year AI partnerships with companies like Bombardier, and grown to a team of 250+.

About the Role
CoLab’s AutoReview product is a core part of how we bring AI and engineering expertise together to improve design quality. So far, much of that work has focused on 2D drawings. Now, we’re building the future of 3D AutoReview — and we need a backend engineer who’s excited to help in that mission.

You’ll join the AutoReview team and help build the systems that power intelligent 3D design analysis at scale. That includes everything from geometry parsing and model analysis to backend infrastructure, rule systems, and AI-assisted review workflows. This is deeply technical, multidisciplinary work that sits at the intersection of backend engineering, CAD systems, and machine learning.

You’ll spend time experimenting, testing assumptions, and solving problems that don’t have obvious answers. You won’t be handed perfectly scoped projects or detailed specs. But you will get ownership, support, and the opportunity to shape how AI-driven design review evolves inside some of the world’s leading engineering organizations.

Our Ideal Candidate
You’ve got solid backend engineering experience, ideally with exposure to 3D CAD modelling or using tools like OpenCascade or Parasolid. You’re comfortable with math-heavy problems (linear algebra, vector calculus), and bonus points if you’ve worked in ML/LLM environments or with HOOPS.

You like to understand the “why” behind the work and communicate it clearly. You thrive in ambiguity and don’t wait for someone else to go first. If something’s unclear, you figure it out—and then explain it to the rest of the team. You’re ready to be the go-to person for 3D anomalous within the AutoReview space and help others get up to speed.

Job Responsibilities

Become a key contributor in the backend development for 3D AutoReview features, from pipeline architecture to production delivery
Experiment with geometry parsing, quality detection, and context generation for 3D model review
Partner with backend, frontend, and product teams to drive the delivery of high-impact features with speed, quality, and cross-functional alignment.
Collaborate with ML teams to incorporate intelligence into the 3D review process
Develop extensible systems for custom rule creation and reference data in 3D AutoReview
Help set and maintain high technical standards across the team

Qualifications

5+ years of experience in backend development, ideally in a product or platform team
Solid grasp of 3D math with practical experience in graphics engines, CAD kernels (HOOPS, OpenCascade, Parasolid), Robotics, 3D simulations, etc.
Hands-on experience designing, developing, and maintaining robust, scalable backend systems and APIs
Experience working with multiple data storage paradigms (SQL, NoSQL, and vector databases)
Experience with ML/AI/LLM concepts and productionalization

Familiarity with 3D model ML pipelines (e.g., feature extraction, embedding generation, model training, or inference)
Familiarity with agentic workflows (e.g., pydantic-ai)
Experience integrating and prompting large language models (LLMs) via APIs (e.g., OpenAI, Anthropic, Google Gemini)
Knowledge of ML frameworks such as PyTorch, Hugging Face, or Scikit-learn
Experience deploying ML services in cloud infrastructure (e.g., AWS SageMaker, Amazon Bedrock, etc.)

Comfortable working with ambiguous or loosely scoped technical challenges
Excellent communicator—able to translate technical problems into clear paths forward
Experience with CAD modeling software like SolidWorks, Creo, or OnShape is a strong plus
Experience with mechanical engineering concepts is a strong plus

Extra Details

This is a full-time, permanent position with a competitive compensation package that includes stock options
Includes extended health benefits, unlimited paid vacation, and RRSP/401K matching and 100% health/dental coverage
Fully remote within North America

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.

Asks for

Values