Staff Engineer - Inference /AI

The Opportunity

About the Team

Your Role

• Architect and develop scalable, containerized, fault-tolerant Kubernetes services for enterprise AI and LLM workloads.
• Build high-performance inference and platform services with low latency and high throughput.
• Design distributed systems, storage, networking, and low-level infrastructure components.
• Develop multi-tenant services for on-premises, hybrid, and cloud AI deployments.
• Implement observability architectures using Prometheus, Grafana, Datadog, OpenTelemetry, and related frameworks.
• Diagnose production issues, perform root-cause analysis, and improve reliability and operational efficiency.
• Build CI/CD pipelines and deployment automation for production-grade services.
• Implement LLM serving capabilities including routing, rate limiting, streaming, load balancing, quotas, and usage budgets.
• Collaborate with globally distributed product, AI, and software engineering teams.
• Contribute across architecture, development, testing, performance analysis, deployment, and operations.
• Review code and designs, provide product feedback, and champion engineering excellence.
• Evaluate emerging technologies and shape the Enterprise AI Platform's technical direction.

Required Qualifications

• 8+ years of experience developing resilient software products in a product development organization.
• Strong fundamentals in data structures, algorithms, operating systems, networking, and distributed systems.
• Hands-on Docker, Kubernetes, and cloud-native architecture experience.
• Production backend development experience with Go, Python, C++, or Rust.
• Experience owning CI/CD pipelines and release automation end to end.
• Understanding of datacenter architecture, including compute, storage, networking, and virtualization.
• Experience deploying software across on-premises, cloud, and hybrid environments.
• Experience designing and tuning high-performance system software.
• Experience using observability and monitoring platforms to resolve production performance issues.
• Familiarity with LLM serving and modern LLM concepts.
• Experience building multi-tenant services on virtualized or containerized infrastructure.
• Strong communication, collaboration, and problem-solving skills, plus a Master's degree in Computer Science or equivalent practical experience.

Bonus Experience

• Machine learning frameworks such as PyTorch or TensorFlow.
• GPU-based systems and acceleration technologies.
• Model-serving platforms such as vLLM, DeepSpeed, Hugging Face TGI, or Triton.
• Retrieval-Augmented Generation, vector databases, and AI orchestration frameworks.
• Open-source contributions, large distributed codebases, production AI platforms, LLM APIs, or agentic systems.
• Production LLM APIs with streaming, prompt guardrails, rate limiting, and usage budgeting.

Highlighted Benefits

• Vancouver benefits include RRSP matching up to 7% of base salary, mental health and paramedical coverage, paid parental leave, RSUs, and an employee stock purchase plan.

Work Arrangement

• The role operates in a hybrid capacity, with employees in Vancouver expected to work onsite at least three days per week under the stated workplace policy.

Pay Transparency

• The expected base salary range at commencement is CAD $171,000 to CAD $257,000 annually.
• Total compensation may include a sign-on bonus, restricted stock units, discretionary awards, and benefits.