Director, Software Engineering

Description

The company is hiring a director to lead the engineering organization behind its compute platform, which spans private and public cloud. The goal is to move the platform toward a scalable, efficient model that uses AI, covering scheduling, capacity management and performance engineering.

This role leads the compute engineering group, including managers and engineers, and owns the technical direction and career growth of that team.

Responsibilities

• Lead the compute engineering organization and provide technical leadership, mentoring and career development for managers and engineers.
• Own the roadmap for the compute scheduler, covering placement, bin-packing, prioritization, preemption and fairness across workloads.
• Define and drive a compute-at-scale model, tracking unit economics such as cost per workload, utilization and capacity headroom.
• Lead capacity management across private and public cloud, including demand forecasting, reservations, procurement and cross-cloud placement.
• Build and grow a performance engineering practice covering 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 beats heuristic approaches.
• Work with product management, SRE, FinOps and infrastructure partners to set priorities and link capacity decisions to cost and customer results.
• Drive engineering quality through architecture reviews, design standards, and modern software and DevOps practices.
• Identify technical and organizational risks that affect several products or platforms, and propose 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 keep a strong engineering organization.
• Share best practices and engineering standards with other teams.
• Define success measures for the first 12 to 18 months, such as utilization gains, scheduler latency targets, forecast accuracy and cost per unit.

Requirements

• Have experience using AI in work processes, decision-making or problem solving, such as AI-powered tools, workflow automation, analysis of AI-driven insights, or assessing AI's impact on your function or industry.
• Have 15+ years of software engineering experience with a Bachelor's degree, 12 years with a Master's degree, or 8 years with a PhD, or equivalent experience.
• Have 5+ years of people management experience, ideally leading software or systems teams in a global 24x7 production environment.
• Have built or operated large-scale schedulers or resource managers such as Kubernetes, Slurm, or internal equivalents.
• Have deep expertise in distributed systems, system design and capacity planning at scale.
• Have hands-on experience running ML in production, including models, feature pipelines or ML platforms. Using AI tools alone does not meet this requirement.
• Have run performance engineering or capacity management across hybrid or multi-cloud estates, including private and public cloud.
• Be fluent in multiple programming languages, such as Java, Python or Go, with strong system design judgment.
• Have experience with CI/CD, DevOps, configuration management at scale, and automation tools.
• Have a deep background in Linux systems architecture and engineering at global scale.
• Have working knowledge of infrastructure components, including hardware, operating systems, virtualization, cloud platforms, web applications and databases.
• Have financial acumen on cloud and infrastructure spend, including cost modeling and FinOps practices.
• Have demonstrated use of AI to improve work processes, decision-making or problem solving.
• Be able to define problems, collect data, establish facts and draw valid conclusions.
• Show strong leadership, communication and cross-functional skills, with a record of building consensus and driving complex projects to completion.
• Optional: experience with ML-driven scheduling, forecasting or capacity optimization.
• Optional: experience with release management and managing large-scale server infrastructure.

Conditions

For roles in this location, base pay is $221,200 to $387,100, plus equity where applicable, variable or incentive compensation, and benefits. Pay is a guideline, and total compensation depends on qualifications, skill level, competencies and work location. Pay varies by geographic location and may change based on work location.

Work persona (flexible, remote or required in office) is assigned based on the nature of the work and the assigned work location. The company may measure the distance between your primary residence and the nearest office using a third-party service to determine eligibility.

Benefits

• Health plans, including flexible spending accounts.
• 401(k) plan with company match and an employee stock purchase plan (ESPP).
• Matching donations, a flexible time-away plan and family leave programs.