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Mlops Machine Learning Engineer Jobs in Vancouver, BC

Manager, Machine Learning Engineering

Vancouver, BC ยท Remote

CA$181K - CA$272K/yr

We are currently seeking a Manager, Machine Learning Engineering to join our rapidly growing ... Collaborate cross-functionally with MLOps engineering, product management, operations, and data ...

About the Role As a Machine Learning Engineer in Agent Factory, you'll design and build the core ML systems behind Workday's next generation of AI agents. Working within a small, senior, cross ...

... machine learning workflows. * Help evaluate and adopt DevOps and MLOps tools that improve system efficiency, observability, developer experience, model deployment, and operational reliability.

Staff Engineer, Computer Vision

Burnaby, BC ยท On-site

CA$105K - CA$140K/yr

Design, develop, train, and integrate advanced computer vision and machine learning solutions ... Experience with cloud AI/ML environments, model training pipelines, or MLOps workflows.

AI Engineer

Vancouver, BC

CA$77K - CA$117K/yr

Practical experience with DevOps and MLOps practices, including Docker, Kubernetes, and CI/CD pipelines for machine learning workloads. * Familiarity with machine learning lifecycle and ...

We're forming small, senior, cross-functional AI teams that bring together product leaders, machine learning engineers, and full-stack builders to create intelligent agents used by millions of people ...

We're forming small, senior, cross-functional AI teams that bring together product leaders, machine learning engineers, and full-stack builders to create intelligent agents used by millions of people ...

Technical Vision, Engineering Leadership, and Execution: Provide executive technical leadership to ... machine learning algorithms, and the end-to-end MLOps lifecycle, including 5+ years of hands-on ...

Showing results 21-40

Mlops Machine Learning Engineer information

What does an MLOps machine learning engineer do?

An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.

What are the key skills and qualifications needed to thrive as an MLOps machine learning engineer?

To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.

How does an MLOps machine learning engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?

An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.

What is the difference between Mlops Machine Learning Engineer vs Data Scientist?

AspectMlops Machine Learning EngineerData Scientist
Required CredentialsBachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps toolsBachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in production environmentsFocus on data analysis, model development, and insights generation
Employer & Industry UsageTech companies, startups, enterprises implementing ML solutionsResearch institutions, analytics firms, tech companies for data insights

While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

Are MLOps machine learning engineers in demand?

MLOps machine learning engineers are in high demand due to the increasing adoption of AI and machine learning across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Docker and Kubernetes. The role offers strong job growth prospects and competitive salaries.

Do MLOps Machine Learning Engineers need a degree?

MLOps Machine Learning Engineers typically do not require a formal degree but often have a background in computer science, data science, or related fields. Practical skills in machine learning, cloud platforms, and tools like Docker, Kubernetes, and CI/CD pipelines are highly valued. Certifications and hands-on experience can also enhance job prospects.

What are popular job titles related to Mlops Machine Learning Engineer jobs in Vancouver, BC?

For Mlops Machine Learning Engineer jobs in Vancouver, BC, the most frequently searched job titles are:

What job categories do people searching Mlops Machine Learning Engineer jobs in Vancouver, BC look for?

The top searched job categories for Mlops Machine Learning Engineer jobs in Vancouver, BC are:

Infographic showing various Mlops Machine Learning Engineer job openings in Vancouver, BC as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 26% Part Time, and 1% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution.

Manager, Machine Learning Engineering

Clio

Vancouver, BC โ€ข Remote

CA$181K - CA$272K/yr

Full-time

Medical, Dental, Vision

Re-posted 13 days ago


Job description

Clio is the global leader in legal AI technology, empowering legal professionals and law firms of every size to work smarter, faster, and more securely.

We are transforming the legal experience for all by bettering the lives of legal professionals while increasing access to justice.

Summary:

We are currently seeking a Manager, Machine Learning Engineering to join our rapidly growing engineering organization. This role is for someone who is passionate about building innovative solutions and being exposed to new challenges and technologies while making an impact. This role is available to candidates across Canada and the US.

What your team does:

We at Clio have an amazing team that is on a mission to transform the legal experience for all, and our engineering team's goal is to deliver an incredible experience to our customers. In the AI team at Clio, we use the latest in GenAI, and LLMs in particular, along with agentic systems, to build solutions that make our customers' work more streamlined and efficient, giving them more time to focus on their clients' needs. That means going beyond single-shot model calls: we design agentic workflows where models reason over context, use tools, and take multi-step actions on behalf of legal professionals, all grounded in a customer's real data.

A day in the life might look like:
  • Lead a team of ML engineers to bring state of the art AI to Clio's clients, spanning traditional ML models, GenAI, and agentic AI.

  • Guide the team in designing and shipping agentic systems, including retrieval, tool use, orchestration, and the evaluation frameworks that keep them reliable and safe in production.

  • Collaborate cross-functionally with MLOps engineering, product management, operations, and data science to identify new tooling for ML and LLM-driven features for Clio customers.

  • Work in an agile environment with our team of ML engineers, ML ops, and full stack developers across a variety of projects

  • Learn new things, challenge yourself, and hone your craft as an ML and infrastructure expert in a space that is moving fast

  • Participate in diverse projects and collaborate with multiple engineering teams across three countries.

  • Review and provide feedback on code, both from within your own team or across all of Clio.

  • Collaborate with teams across Clio to diagnose, understand, and solve problems, and to build solutions that may span many areas.

  • Teach and learn from those around you, providing constructive feedback and taking on feedback to help grow.

What you may have:
  • Experience in managing high performing teams.

  • Experience with technical evaluations of various ML and LLM products, vendors, out-of-the-box solutions, and conducting quick proof of concepts if necessary.

  • In-depth understanding of LLMs, GenAI, and the competitive landscape, including where the technology is heading.

  • Hands-on familiarity with building agentic systems, such as tool-calling agents, RAG pipelines, prompt and context design, and evaluating agent behavior at scale.

  • Experience in fine-tuning foundational models and/or training language models in-house.

  • Experience to manipulate, clean, and pre-process complex unstructured data for model development.

  • The ability to become fluent in new technologies quickly and work effectively in an ever-evolving environment that includes distributed teams and customers.

  • Demonstrated success in mentorship in software development, particularly using an Agile process and with large scale SaaS products.

  • A diverse base of knowledge that allows you to help your team solve complex technical problems.

  • A history of past projects (including notable successes and lessons learned).

  • Clear and concise communication skills and the ability to build high-trust relationships with fellow Clions and customers.

#LI-Remote

This role is a backfill for an existing position.

What you will find here:

Compensation is one of the main components of Clio's Total Rewards Program. We have developed a series of programs and processes to ensure we are creating fair and competitive pay practices that form the foundation of our human and high-performing culture.

Some highlights of our Total Rewards program include:

  • Competitive, equitable salary with top-tier health benefits, dental, and vision insurance

  • Hybrid work environment, with expectation for local Clions (Vancouver, Calgary, Toronto, Dublin, London, New York City and Sydney) to be in office min. twice per week.

  • Flexible time off policy, with an encouraged 20 days off per year.

  • $2000 annual counseling benefit

  • RRSP matching and RESP contribution

  • Clioversary recognition program with special acknowledgement at 3, 5, 7, and 10 years

The expected salary range for this role is $181,360 to $272,040 CAD. Initial placement within the range is informed by geographic region, experience, and skillset, with room to progress as impact and tenure grow. Final offer amounts will vary based on candidate profile.

Diversity, Inclusion, Belonging and Equity (DIBE) & Accessibility

Our team shows up as their authentic selves, and are united by our mission. We are dedicated todiversity, equity and inclusion. We pride ourselves in building and fostering an environment where our teams feel included, valued, and enabled to do the best work of their careers, wherever they choose to log in from. We believe that different perspectives, skills, backgrounds, and experiences result in higher-performing teams and better innovation. We are committed to equal employment and we encourage candidates from all backgrounds to apply.

Clio provides accessibility accommodations during the recruitment process. Should you require any accommodation, please let us know and we will work with you to meet your needs.

Learn more about our culture atclio.com/careers

We're a Human and High Performing AI company, meaning we use artificial intelligence to improve all of our operations. In recruitment, AI helps us streamline the process for greater efficiency. However, we've built our systems to ensure that a human always reviews AI-generated output, and we never make automated hiring decisions.

Disclaimer: We only communicate with candidates through official @clio.com email addresses.