About the Team & RoleThe AI Platform team is responsible for building the foundational infrastructure that powers intelligent experiences across Mozilla products. This includes model training pipelines, high-throughput inference services, GPU orchestration, and secure, privacy-respecting AI systems that operate reliably at global scale.
We're looking for a Senior Software Engineer with a strong platform mindset to help design, build, and operate Mozilla's AI platform. In this role, you'll work at the intersection of machine learning, distributed systems, and production infrastructure-ensuring that models can be trained, deployed, and served efficiently, securely, and at scale. You will collaborate closely with product, infrastructure, and security teams to enable fast iteration while meeting strict performance and privacy requirements.
What You'll Do- Design, build, and operate core platform services and APIs used to deploy and serve production workloads at scale.
- Own service reliability end-to-end, driving improvements in availability, scalability, performance, and operational excellence.
- Lead efforts to optimize backend services for throughput, latency, and cost efficiency across distributed infrastructure.
- Design and manage Kubernetes-based workloads, including GitOps deployment pipelines, environment configuration, and resource utilization optimization.
- Own and improve critical parts of the service lifecycle, including packaging, versioning, testing strategies, validation, and deployment automation.
- Implement and evolve observability practices (metrics, logging, tracing, alerting) to improve visibility and operational resilience of backend services and pipelines.
- Partner closely with product, infrastructure, security, and data teams to design scalable platform capabilities that enable new product features.
- Contribute to technical design discussions, propose architectural improvements, and mentor junior engineers through code reviews and knowledge sharing.
- Participate in and help improve operational processes, including incident response, on-call rotations, and post-incident reviews.
What You'll Bring- Bachelor's degree with 4-6 years of relevant industry experience, or Master's degree with significant hands-on experience building and operating production systems, or work experience equivalent
- Strong, modern Python skills, with experience writing clean, maintainable code and working with a fast toolchain (dependency management, linting, formatting, type checks, pre-commit), building both libraries and CLIs that output structured data.
- Advance experience with database deployment and management, bonus points for familiarity with Postgres
- Proven experience deploying and operating workloads in cloud environments, including production-grade infrastructure on GCP and GKE (artifact registries, managed caches, networking and internal load balancing, VPC, DNS, and separation of nonprod and prod).
- Hands-on experience with Kubernetes and Helm, writing charts that deploy across environments with per-environment configuration and progressive feature rollout.
- Experience with Terraform for provisioning infrastructure across environments, including schema validation and PR-level plan review.
- Experience designing and running scalable APIs that hold up under load, including health and readiness checks, auth, and clean startup and shutdown.
- Experience with Grafana or similar tools for metrics, dashboards, and reading application and infrastructure health together during rollouts.
- Strong problem-solving skills and the ability to debug performance and reliability issues in distributed systems.
- Clear and effective communication skills, with experience collaborating across engineering, product, and infrastructure teams.
- On-call experience, including participating in incident response and post-incident reviews.
Bonus Skills- Experience with Ray or Ray Serve for GPU-backed model serving, including setting resource requests and replica counts aligned with available hardware.
- Experience building stateless ML services such as embedding or similarity models, including multi-model loading, runtime device selection, batch APIs, and handling model-cache and cold-start tradeoffs.
- Experience running a multi-provider LLM gateway, including routing between providers, migrating models, and mixing self-hosted with third-party serving.
- Familiarity with containerization and orchestration systems in production environments beyond core Kubernetes/Helm usage.
- Exposure to privacy-preserving ML techniques, security best practices, or responsible AI system design.
- Contributions to open-source infrastructure projects or leadership in building reusable internal tooling.
What you'll get:- Generous performance-based bonus plans to all eligible employees - we share in our success as one team
- Rich medical, dental, and vision coverage
- Generous retirement contributions with 100% immediate vesting (regardless of whether you contribute)
- Quarterly all-company wellness days where everyone takes a pause together
- Country specific holidays plus a day off for your birthday
- One-time home office stipend
- Annual professional development budget
- Quarterly well-being stipend
- Considerable paid parental leave
- Employee referral bonus program
- Other benefits (life/AD&D, disability, EAP, etc. - varies by country)
Hiring Ranges:Canada Tier 1 Locations
$104,000-$139,000 CAD
Canada Tier 2 Locations
$95,000-$126,000 CAD
