Software Engineer, Systems Machine Learning

Meta is seeking a Software Engineer to build and maintain developer tooling for its MTIA AI accelerators. The role spans debugging, profiling, performance and memory analysis, and monitoring, while collaborating with compiler, runtime, kernel, hardware, and product teams.

Responsibilities

• Design and develop software tools across the MTIA tooling ecosystem, including debugging, profiling, performance analysis, memory analysis, and monitoring infrastructure
• Own features and components end-to-end, from design through implementation, testing, and deployment
• Build and improve tooling infrastructure that enables engineers to efficiently develop, test, and optimize AI workloads on MTIA hardware
• Collaborate with the MTIA compiler, runtime, kernel, and hardware teams to integrate tooling hooks and support new platform capabilities
• Proactively identify gaps in the tooling ecosystem and propose solutions to improve developer productivity
• Contribute to technical design discussions, write design documents for medium-scope projects, and participate in code reviews
• Partner with internal product teams across advertising, recommendations, and generative AI to understand developer pain points and improve tool usability

Preferred

• Exposure to accelerator ecosystems (GPU/CUDA, TPU, custom ASICs) or heterogeneous computing environments
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience building developer tools such as debuggers, profilers, build systems, CLI tools, monitoring dashboards, or diagnostic utilities
• Contributions to open-source projects demonstrating tooling or system software interest
• Experience in using data-driven methods to evaluate tooling effectiveness and to prioritize improvements
• Familiarity with Linux debugging and profiling tools (gdb, perf, strace, eBPF, valgrind) or similar diagnostic infrastructure
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Familiarity with ML frameworks (PyTorch, TensorFlow) or compiler infrastructure (LLVM, MLIR, TVM)
• Experience with distributed systems debugging, profiling, or monitoring at scale
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• GPU/CUDA
• TPU
• Custom ASICs
• Heterogeneous Computing
• Prompt Engineering
• Context Engineering
• Agent Orchestration
• Emerging AI Technologies
• Developer Tool Development
• Debugger Development
• Profiler Development
• Build Systems
• CLI Tool Development
• Monitoring Dashboards
• Diagnostic Utilities
• Open-Source Contributions
• System Software
• Data-Driven Evaluation
• Tooling Effectiveness Measurement
• Linux Debugging
• Linux Profiling
• gdb
• perf
• strace
• eBPF
• valgrind
• Responsible AI Practices
• AI Ethics
• Risk Assessment
• Bias Mitigation
• AI Quality Reviews
• AI Accuracy Reviews
• PyTorch
• TensorFlow
• LLVM
• MLIR
• TVM
• Distributed Systems Debugging
• Distributed Systems Profiling
• Distributed Systems Monitoring
• AI Tool Integration
• Workflow Optimization
• Workflow Redesign
• Impact Measurement
• Continuous Learning
• Analytical Thinking
• Prioritization

Benefits

• Bonus
• Equity

Company

• Social networking and immersive augmented/virtual reality technologies.
• Menlo Park, California, USA
• https://www.meta.com/
• Founded in 2004
• 10001+ employees

Qualifications

• Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience
• 2+ years of experience in software engineering, with exposure to systems software, developer tooling, or infrastructure
• Proficiency in C++ and Python, including systems-level programming concepts
• Experience working across multiple layers of a system stack (e.g., application, runtime, OS/driver, or hardware interfaces)
• Track record of independently delivering software projects from design through production deployment
• Experience debugging and resolving issues in complex software systems (e.g., using log analysis, stack traces, or system-level diagnostic tools)
• Exposure to accelerator ecosystems (GPU/CUDA, TPU, custom ASICs) or heterogeneous computing environments
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience building developer tools such as debuggers, profilers, build systems, CLI tools, monitoring dashboards, or diagnostic utilities
• Contributions to open-source projects demonstrating tooling or system software interest
• Experience in using data-driven methods to evaluate tooling effectiveness and to prioritize improvements
• Familiarity with Linux debugging and profiling tools (gdb, perf, strace, eBPF, valgrind) or similar diagnostic infrastructure
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Familiarity with ML frameworks (PyTorch, TensorFlow) or compiler infrastructure (LLVM, MLIR, TVM)
• Experience with distributed systems debugging, profiling, or monitoring at scale
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

Required skills

• C++, Developer Tooling, EBPF, GPU/CUDA, Gdb, Linux Debugging and Profiling, Perf, PyTorch, Python, Systems Software, Systems-Level Programming, TPU

Experience

• 2+ years