Who you are

- Experience in leveraging or critical thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry

- 15+ years of software engineering experience with a Bachelor's degree; or 12 years with a Master's degree; or a PhD with 8 years of experience; or equivalent experience

- 5+ years of people management experience, ideally leading software or systems engineering teams in a global 24x7 production environment

- Experience building or operating large-scale schedulers or resource managers, such as Kubernetes, Slurm, or internal equivalents

- Deep expertise in distributed systems, system design, and capacity planning at scale

- Hands-on experience applying ML in production, including models, feature pipelines, or ML platforms. Using AI tools alone is not enough

- Experience running performance engineering or capacity management across hybrid or multi-cloud estates, including private cloud and public cloud platforms

- Fluency in multiple programming languages (such as Java, Python, or Go) and strong system design judgment

- Experience with CI/CD, DevOps, configuration management at scale, and automation tools

- Deep background in Linux systems architecture and engineering on a global scale

- Working knowledge of infrastructure components, including hardware platforms, operating systems, virtualization, cloud platforms, and web applications and databases

- Financial acumen on cloud and infrastructure spend,



including cost modeling and FinOps practices

- Demonstrated experience using AI to improve work processes, decision-making, or problem solving

- Ability to define problems, collect data, establish facts, and draw valid conclusions

- Robust leadership, communication, and cross-functional skills, with a record of building consensus and driving complex projects to completion

- Experience with ML-driven scheduling, forecasting, or capacity optimization

- Experience with release management and managing large-scale server infrastructure

What the job involves

- Director, Software Engineering Compute will lead the engineering organization that builds and operates our compute platform. You will evolve it into a scalable, efficient, and AI-driven compute model spanning private and public cloud, covering scheduling, capacity management, and performance engineering

- Lead the compute engineering organization, providing technical leadership, mentoring, and career development for managers and engineers

- Own the roadmap for the compute scheduler, including placement, bin-packing, prioritization, preemption, and fairness across workloads

- Define and drive the compute-at-scale model, including unit economics such as cost per workload,



utilization, and capacity headroom

- Lead capacity management for private and public cloud: demand forecasting, reservations, procurement, and cross-cloud placement

- Establish and scale a performance engineering practice: benchmarking, load and scale testing, regression detection, and SLOs for scheduling latency and throughput

- Apply AI and ML to demand forecasting, anomaly detection, and placement optimization. Set the AI/ML roadmap and decide where it outperforms heuristics

- Work with product management, SRE, FinOps, and infrastructure partners to set priorities and tie capacity decisions to cost and customer outcomes

- Drive engineering excellence through architecture reviews, design standards, and modern software and DevOps practices

- Anticipate technical and organizational risks that affect multiple products or platforms, and provide clear, structured mitigations

- Assess availability, scalability, and operational risk across compute systems, and lead the optimization work that follows

- Manage resources, staffing, and succession planning to build and retain a best-in-class engineering organization

- Share best practices and engineering standards with other teams across the organization

- Define success measures for the first 12 to 18 months, such as utilization gains, scheduler latency targets, forecast accuracy, and cost per unit

Benefits

- Generous family leave

- Matched donations

- Annual learning stipends

- Flexible PTO

- Competitive retirement plan

- Paid volunteer time

📌 Director of Software Engineering (Canada)
🏢 ServiceNow
📍 Canada