Engineering Manager, AI Developer Technology

What matters most

• End-to-end AI performance

  Lead engineers improving real-world deep learning training and inference, including complex parallel workloads across NVIDIA GPUs, CPUs and interconnects. The posting emphasizes linear algebra, architecture fundamentals and low-level optimization.

• Technical direction and execution

  Set short- and long-term team goals and lead critical initiatives to develop algorithms and advance accelerated machine learning applications.

• Cross-functional technical influence

  Work with customers and NVIDIA research, hardware and software teams on solutions and on the design of future hardware, software and programming models. Communicate design decisions and project plans to internal and external collaborators.

• Team building and engineering excellence

  Hire, coach and develop engineers while fostering a diverse, skilled team and a culture of engineering excellence.

Likely challenges to think through

• Finding the right optimization target

  Potential challenge: an improvement to one computational primitive may not improve end-to-end performance across LLM, vision, speech, recommender and multimodal workloads. Prepare to explain how you would measure bottlenecks across training or inference, identify the limiting GPU, CPU or interconnect component, and validate an algorithmic change against application-level results.

• Balancing immediate gains with platform direction

  Potential challenge: customer-facing performance work may compete for attention with longer-term initiatives that inform future platform design. Prepare an example of how you would set priorities, define near- and long-term goals, and turn technical findings into useful input for research, hardware and software collaborators.

• Leading without losing technical depth

  Potential challenge: the manager must make sound low-level technical decisions while giving engineers room to own solutions and grow. Prepare to discuss how you would review optimization decisions, communicate trade-offs, coach engineers and allocate your time between technical leadership and hiring.

Topics to prepare

• Training and inference performance

  Posting: Requires in-depth expertise in linear algebra and optimizing deep learning training and inference. Suggested exercise: Compare the computation and data-movement costs of a matrix multiplication during training versus inference, then explain which changes might improve end-to-end performance.

• CPU, GPU and interconnect architecture

  Posting: Requires CPU and GPU architecture knowledge; the team optimizes workloads across GPU, CPU and interconnects. Suggested exercise: Sketch where computation, memory access and communication occur in a parallel workload, and explain how each could limit performance.

• Parallel algorithms and low-level optimization

  Posting: Calls for optimizing complex parallel algorithms and requires a parallel-programming background. Suggested exercise: Explain how work partitioning, synchronization and data placement affect the performance of one deep learning operation.

• Technical initiative strategy

  Posting: The manager sets short- and long-term goals and leads critical accelerated-ML initiatives. Suggested exercise: Outline a sequence of goals for improving one application's end-to-end performance, including how you would decide what to tackle first.

• LLM and multimodal computational primitives

  Posting: LLM and multimodal architecture expertise, including optimization of relevant primitives, is preferred rather than required. Suggested exercise: Choose a computational primitive in one of these architectures and explain its likely performance constraints.

Skills to revise

• C/C++ algorithm implementation

  Posting: Requires C/C++ fluency and a deep understanding of algorithms and software development. Suggested exercise: Write and review a C/C++ matrix operation, checking correctness and identifying opportunities to reduce unnecessary memory access.

• Parallel-programming implementation

  Posting: Names CUDA, OpenMP, MPI and pthreads as examples of relevant parallel-programming backgrounds. Suggested exercise: Using one named approach you know, implement or outline a parallel matrix operation and explain its partitioning and synchronization choices.

• Performance investigation

  Posting: The team improves end-to-end application performance, including training and inference workloads. Suggested exercise: For a workload you can discuss, compare a baseline with one optimization, record the end-to-end effect and explain any trade-off between local and overall gains.

• Technical planning and communication

  Posting: Requires written and presentation skills and communication of design decisions and plans to internal and external collaborators. Suggested exercise: Draft a one-page optimization proposal stating the bottleneck, alternatives, decision, risks and measurement plan; then present it briefly.

• Engineering leadership and collaboration

  Posting: The manager coaches and hires engineers and works with customers and NVIDIA research, hardware and software teams. Suggested exercise: Rehearse a concrete example of setting a team goal, coaching an engineer and resolving a technical trade-off with collaborators; distinguish your decisions from the team's contributions.

• Map the stated qualifications to evidence

  The posting asks for relevant education or equivalent experience, 8+ years overall, 5+ years technical, and 3+ years in engineering leadership. Prepare a brief timeline and specific examples for each requirement; leave any gap or uncertainty explicit.

• Build an end-to-end performance walkthrough

  Prepare one real deep learning training or inference example showing the workload, initial bottleneck, measurements, optimization decisions, and observed result. Identify where linear algebra and CPU, GPU, or interconnect behavior mattered; use only results you can substantiate.

• Rehearse a parallel-algorithm design exercise

  As practice, choose a complex ML workload and sketch how you would diagnose and improve its performance on GPU and CPU platforms. Be ready to explain algorithmic trade-offs, low-level architecture considerations, and relevant C/C++ and parallel-programming choices.

• Document a critical initiative you led

  The role sets short- and long-term technical goals. Prepare a concise account of an initiative’s goals, prioritization, milestones, technical decisions, team responsibilities, and outcomes, distinguishing your leadership from the team’s work.

• Prepare a cross-functional communication example

  The role works with customers and NVIDIA research, hardware, and software teams. Bring a short, shareable design summary or project-plan example showing how you explained trade-offs, handled differing needs, and incorporated feedback.

• Collect hiring and coaching examples

  Prepare specific examples of how you recruited, developed, or coached engineers and promoted engineering excellence. The posting treats recruiting top talent as a preferred qualification, so distinguish direct recruiting experience from other management work.

• Draft questions about the team’s priorities

  Ask which application workloads are most pressing, how the team measures end-to-end gains, how customer findings reach hardware and software teams, and what short- versus long-term goals this manager would own. These are questions to clarify the role, not assumptions about current projects.