Software Engineer, MTIA SW Performance Autotuning

Meta; Menlo Park (CA); On-site; USD 210,000 - 320,000; Full time

Job summary

Meta is seeking an experienced engineer to lead performance autotuning on MTIA, Meta's custom training and inference accelerator. You will head the MTIA Software Performance Autotuning team within Infra Foundations and own how we extract maximum performance from hardware, across kernels, graphs, and runtime configurations.

You will set autotuning strategy, design the search and benchmarking framework, and collaborate with compiler, kernel, and product teams to ship speedups.

Qualifications

• Bachelor's degree in CS/CE or equivalent practical experience.
• Strong Python and C++ skills with hands-on experience across the PyTorch stack.
• 4+ years in ML systems, AI infra, performance engineering, or similar.

Responsibilities

• Lead performance autotuning on MTIA to maximize hardware and software efficiency.
• Define autotuning strategy, build search and benchmarking infrastructure, and guide cross-team collaboration.
• Partner with compiler, kernel, runtime, and product teams to ship speedups.

Skills

Python

C++

ML systems

Cross-team collaboration

Mentoring

Performance engineering

Roofline analysis

Experimentation

Technical leadership

Education

BS in CS/CE or related field

MS/PhD in CS/CE or related field

Tools

TorchInductor

torch.compile

MLIR

XLA

TVM

Triton

Ansor

AutoTVM

Job description

We are looking for an experienced engineer to lead performance autotuning on MTIA — Meta's custom training and inference accelerator. You will lead the MTIA Software Performance Autotuning team (part of Infra Foundations) and own how we extract maximum performance from our hardware, automatically and at scale. Every kernel, every compiled graph, and every runtime configuration has a large space of possible implementations — tile sizes, scheduling, memory layouts, fusion decisions, precision choices — and the right one depends on the chip, the model, and the shape. Hand-tuning does not scale. The team's core mission is to make MTIA fast by default: building the search infrastructure, cost models, and tuning methodology that finds the best configuration without a human in the loop. As a technical leader, you will define our autotuning strategy, architect the search and benchmarking infrastructure, and partner closely with compiler, kernel, runtime, and product (e.g., Ads Ranking, Recommendation Systems, GenAI) teams to turn performance headroom into shipped speedups.

Autotuning operates across the full MTIA software stack — FX graphs, compiler, kernels, runtime, PyTorch — which means lots of cross-team collaboration. We partner closely with machine learning engineers across Ads, Instagram/Facebook, and Meta Superintelligence Labs teams whose models run on MTIA.

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Strong Python and C++ skills, with hands-on experience across the PyTorch stack
• 4+ years in ML systems, AI infra, performance engineering, or similar
• Experience driving problems that span multiple teams, where no one owns the whole picture
• Experience with accelerator performance concepts — roofline analysis, memory bandwidth, occupancy, and what makes kernels fast or slow on hardware
• Track record of setting technical direction and mentoring engineers
• BS in CS, CE, Math, or equivalent experience
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience with production ML models (recommender systems, LLMs, ranking) is useful but not required
• Hands-on experience with torch.compile, TorchInductor, or other ML compiler stacks (XLA, TVM, MLIR, Triton)
• Experience with autotuning, cost models, or search-based optimization (e.g., Ansor, AutoTVM, learned schedulers)
• MS or PhD in CS, CE, compilers/systems, or related
• Experience with hardware bring-up or accelerator development (GPU, TPU, or custom ASIC)
• Experience with kernel-level performance optimization — tiling, scheduling, memory layout, fusion, and how they interact on real hardware