Top Engineering Manager Jobs

Leads an engineering team responsible for design reviews, plan reviews, technical analysis, estimates, and compliant engineering deliverables. Hires, coaches, evaluates, and develops staff while improving departmental processes and quality. Builds client and agency relationships, addresses technical inquiries, supports revenue growth, ensures compliance with state and HUD requirements, and manages workloads, billing oversight, continuing education, and succession planning.

Lead a senior engineering team building customer-facing products, AI-powered analytics, and infrastructure. This hands-on manager will guide architecture, product execution, engineering practices, team growth, and AI-enabled software development. The role requires strong technical judgment, ownership, autonomy, and experience leading senior engineers in a high-trust environment.

Lead and grow a machine learning and software engineering team building production LiDAR and radar perception systems for autonomous trucks. Own technical direction, roadmap, model development, evaluation, integration, deployment, and monitoring. Guide multitask perception, sensor fusion, robustness, and graceful degradation across adverse conditions. Collaborate cross-functionally, manage dependencies and milestones, maintain engineering standards, and communicate risks and progress to leadership.

Leads KBRA’s Business Applications function, owning enterprise SaaS strategy, governance, lifecycle management, integrations, identity access, operational support, and application modernization. The manager administers platforms such as Microsoft 365, Slack, Zoom, Box, and Atlassian; configures Entra ID SSO and provisioning; manages vendors and escalations; develops roadmaps and metrics; partners with business and technology stakeholders; and mentors a small team while remaining hands-on in engineering, troubleshooting, automation, and project delivery.

Lead execution of SoC development from concept to production by coordinating IP design, integration, DFT, physical design, emulation, silicon bring-up and software teams. Track milestones, identify technical risks, drive mitigations, provide technical leadership, implement data analytics/automation to improve execution, and run after-action reviews to continuously improve workflows across a global, matrixed engineering organization.

Leads a team responsible for petabyte-scale storage deployment across on-premises data centers and AWS, Azure, and GCP. Owns storage design, procurement, installation, automation, monitoring, capacity planning, hardware refreshes, lifecycle management, secure decommissioning, and data retention policies. Develops self-service dashboards, coordinates infrastructure capacity planning, manages vendors, evaluates new technologies, and establishes engineering standards, runbooks, and on-call practices while mentoring storage deployment engineers.

Lead NVIDIA’s HBM Product Development Engineering team through bringup, characterization, NPI, manufacturing ramp, and high-volume production. Manage engineers, collaborate with ASIC, test, reliability, manufacturing, and HBM suppliers, and resolve yield excursions, quality issues, and reliability escapes. Establish data analysis, automation, and statistical monitoring frameworks; drive supplier feedback, test audits, qualification readiness, and manufacturing scale-up. Present HBM readiness and risk assessments to leadership.

Lead NVIDIA’s RL post-training frameworks strategy and engineering ecosystem across distributed training, inference, rollout, evaluation, orchestration, and NVIDIA platforms. Build and manage globally distributed teams, prioritize upstream and internal investments, establish benchmarks and execution metrics, and drive reliable open-source integrations. Partner with research, product, hardware, CUDA, networking, and external communities to scale reinforcement learning workloads across GPUs and heterogeneous systems.

Leads NVIDIA’s global Compute Core Engineering organization, managing talent and defining infrastructure strategy across data centers, cloud environments, and compute platforms. Oversees modernization, automation, reliability, scalability, security, disaster recovery, capacity forecasting, and lifecycle management for Linux and foundational infrastructure services. Drives self-service platforms using Go, Python, Terraform, and GitOps, establishes service health metrics, leads incident reviews, and partners with leadership and internal customers to deliver resilient infrastructure products.

Drive NVIDIA platform adoption across the CAE ISV and partner ecosystem. Develop ecosystem growth, promotion, and co-selling strategies; lead technical collaborations, proof-of-value engagements, business alliances, and global cross-functional programs. Partner with engineering, product, marketing, sales, OEMs, CSPs, channel partners, and customers to increase revenue, shape product direction, and communicate NVIDIA’s CAE, AI-Physics, and Agentic AI value proposition. Support customer announcements, speaking sessions, and keynote activities.

Manage a US-based software engineering team developing NVIDIA networking products. Responsibilities include debugging complex embedded networking issues, creating diagnostic tools and processes, engaging customers on technical requirements, and collaborating globally with engineers, architects, product managers, and field teams. The role requires embedded networking software expertise, people management experience, strong C and Python skills on Linux, and leadership in AI-native software development.

Leads a mechanical engineering team developing complex systems for AI, robotics, and self-driving vehicle initiatives. Responsibilities include defining project requirements, coordinating mechanical system development, delivering designs on schedule and within budget, promoting continuous improvement, and mentoring engineers. The role requires strong mechanical design, CAD, engineering analysis, cross-functional collaboration, and leadership expertise.

Leads engineering build activities for new NVIDIA products, coordinating multifunctional teams, prototype hardware planning, supplier and vendor interactions, resource allocation, risk management, logistics, testing, reporting, and prototype distribution. Ensures boards and systems are delivered with appropriate quality, configuration, timing, and quantity for development and testing. Improves build processes and infrastructure while supporting successful project releases into manufacturing.

Lead and mentor an AI agent engineering team developing scalable, secure HR and Finance AI solutions. Drive technical strategy, project delivery, platform integration, and adoption of LLMs, RAG, and orchestration frameworks such as LangGraph. Partner with product, data science, architecture, vendors, and open-source communities while ensuring privacy, security, compliance, testing, monitoring, reliability, and performance.

Lead technical programs for NVIDIA’s enterprise network engineering and infrastructure teams. Guide architects, align engineering programs and processes, review designs, prioritize network deployments, manage office and data center builds, identify risks, optimize costs, and drive cross-functional solutions. The role requires extensive network engineering or technical program management experience, hands-on deployment expertise, scalable enterprise network design knowledge, and strong communication across global teams.

Leads the Kubernetes Runtime Engineering team responsible for tenant cluster configuration, container runtime reliability, GPU workload infrastructure, networking, storage, security, and multi-tenancy isolation. Manages engineers, drives architecture for fleet-scale cluster lifecycle operations, builds AICR lifecycle tooling, establishes hardening standards, partners across platform and cybersecurity teams, and represents runtime engineering in architecture, roadmap, customer, and open-source initiatives.

Leads software engineering teams delivering rack-scale infrastructure software, including control planes, lifecycle orchestration, system health, recovery, and fleet-scale reliability. Owns planning, staffing, releases, quality, production support, open-source delivery, and cross-functional coordination across hardware, firmware, networking, security, operations, customers, and partners. Builds technical leadership, manages engineering performance, and guides architecture and software quality.

Manage silicon bring-up through production release, including wafer forecasts, schedules, chip and board allocations, prototype delivery, engineering milestones, validation tasks, risk mitigation, and cross-functional communication. Improve operational processes, implement software tools for analytics, and coordinate technical and business teams during new product introduction.

Develop and lead datacenter product strategies and execution for AI, high-performance computing, power solutions, and server platforms. Own programs from concept through mass production, coordinate JDM partners and suppliers, establish requirements and schedules, lead multifunctional teams through system, hardware, and software integration, and drive validation, quality, automation, and continuous improvement.

Leads NVIDIA’s Developer Infrastructure and Build Engineering organization, overseeing DevOps, CI/CD, build systems, developer platforms, and employee-facing software delivery. Responsibilities include hiring and mentoring engineers, setting technical strategy, managing architecture and roadmaps, improving developer productivity, and operating reliable, secure, business-critical services. The role requires deep expertise in C++, CUDA, build systems, distributed software, release processes, and large-scale CI/CD platforms, along with extensive engineering leadership experience.

Leads a software engineering team developing, optimizing, and validating Linux GPU device drivers. Partners with engineering, product, program management, and external stakeholders to define roadmaps, understand requirements, and improve development processes. Requires strong Linux system software, kernel, C/C++, debugging, computer architecture, operating systems, hardware-software interaction, performance, security, and scalability expertise.

Leads engineering enablement for large-scale AI/HPC clusters, managing a support team, training and documentation, request intake and prioritization, specialist debugging coordination, and user community development. Partners across architecture, software, firmware, networking, storage, datacenter operations, performance, and program teams. Uses service metrics and user feedback to improve workflows, self-service guidance, resource allocation, and support quality.

Leads the architecture, development, and productionization of scalable data and AI platforms for supply chain operations. Manages engineers while remaining hands-on with cloud data engineering, ETL/ELT, streaming, AI agents, RAG, vector search, and advanced analytics foundations. Oversees delivery across concurrent initiatives, partners with business and technical stakeholders, mentors teams, and ensures data quality, observability, security, and production reliability.

Leads engineering teams developing platform software for GPU-accelerated applications using Kubernetes, container runtimes, Kata Containers, and Confidential Containers. Responsibilities include enabling GPUs as Kubernetes resources, integrating software across NVIDIA’s technology stack, managing priorities, and delivering secure, reliable distributed systems. The role requires deep systems software expertise, engineering management experience, and hands-on knowledge of containers, Linux, cloud computing, virtualization, and confidential computing technologies.

Leads the Graphics APIs and Platforms team, owning technical direction, roadmap, quality, and delivery across console graphics, compute-based graphics, Slang, drivers, and platform software. Drives hardware enablement, performance optimization, validation, and releases while collaborating with GPU architects and platform partners. Develops engineers, strengthens hiring and engineering practices, and advances graphics APIs, shading languages, and AI-assisted development for next-generation GPUs.