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Job Description
The role involves designing, building, and maintaining large-scale machine learning infrastructure to support safety and operational AI features across various industries, with a focus on scalability, reliability, and cross-team collaboration.
Key Responsibilities
- Design, build, and operate the end-to-end machine learning platform for training, experimentation, inference, and edge deployment.
- Partner with product and ML teams to develop and improve ML-powered features that enhance safety, reliability, and cost efficiency.
- Lead capacity planning and throughput estimation for new ML features from exploration to production.
- Collaborate on experiment design, evaluation, and interpretation to inform product and technical decisions.
- Evolve and standardize infrastructure for training, experimentation, and deployment workflows.
- Design and operate scalable online and batch inference systems, ensuring observability and reliability.
- Work with firmware and edge teams to package, validate, and deploy models to devices, establishing feedback loops for continuous improvement.
- Ensure the reliability, security, and observability of ML systems across cloud and edge environments.
- Provide technical leadership by setting architecture strategies, influencing cross-team decisions, and mentoring engineers and scientists.
Requirements
- 10 years of overall experience in machine learning engineering or related fields, with a strong track record of building and operating large-scale ML systems
- Strong experience with distributed computing frameworks such as Ray and/or Spark
- Hands-on experience with cloud infrastructure including AWS, containers such as Kubernetes, and production observability tooling
- Proven experience building or supporting ML platforms used by multiple teams for training, experimentation, or inference
- Solid understanding of ML fundamentals including evaluation, experiment design, and model iteration in production environments
- Experience shipping ML-powered features end-to-end, from design through production and iteration, with measurable impact on product or business metrics
- Background in computer vision and/or LLM-based systems in production environments
- Experience with edge or on-device ML and collaboration with firmware or embedded teams
- Familiarity with model lifecycle systems including model registry, deployment, monitoring, rollback, and drift detection
- Experience working in environments with strong security and compliance requirements
- Demonstrated ability to lead across teams and influence technical direction at Staff scope
- A strong sense of ownership and a desire for end-to-end autonomy from platform design to real-world impact
Benefits & Perks
Annual Base Salary 196,000 - 269,500 CAD
Performance-based bonus variable pay
Equity with no vesting cliff and ongoing refresh opportunities
Flexible, employee-led remote work model
Comprehensive health and parental leave plans
Professional development stipend
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