Engineer (AI/ML Systems)

Location: Sunnyvale, California, United States; hybrid work arrangement.

Company

hirify.global builds large-scale AI hardware and software systems designed to accelerate machine learning training and inference beyond conventional GPU architectures.

What you will do

• Build analytical and empirical performance models for advanced ML training and inference algorithms.
• Analyze how model size, sequence length, batch size, parallelism, and hardware scale affect algorithmic trade-offs.
• Construct Pareto frontiers covering model quality, latency, throughput, memory, communication, and compute cost.
• Develop prototypes and benchmarks for the hirify.global WSE and relevant GPU or software baselines.
• Identify kernel, compiler, runtime, communication, and algorithmic bottlenecks through profiling and system analysis.
• Partner with research, kernel, compiler, runtime, inference, and architecture teams on implementation and hardware–software co-design decisions.

Requirements

• Degree or equivalent practical experience in computer science, computer engineering, electrical engineering, mathematics, or a related field.
• Strong knowledge of computer architecture, parallel computing, systems performance, machine learning fundamentals, and ML systems.
• Experience with analytical performance modeling, algorithmic complexity analysis, benchmarking, or system simulation.
• Strong first-principles reasoning about compute, memory, communication, accuracy, and scaling behavior.
• Proficiency in Python and comfort with C++.
• Experience profiling and debugging performance in ML, HPC, CPU, GPU, or accelerator-based systems.

Nice to have

• Experience with roofline analysis, CPU or GPU simulators, kernel optimization, or hardware–software co-design.
• Familiarity with CUDA, Triton, PyTorch, JAX, or open-source LLM training and inference systems.
• Knowledge of transformer internals, attention variants, KV-cache strategies, model parallelism, sparsity, quantization, or parallel generation.
• Research publications, patents, or significant open-source contributions in ML systems, computer architecture, or computational efficiency.
• Experience evaluating technology choices for future hardware or software architectures.

Culture & Benefits

• Work on an AI platform designed to go beyond GPU constraints.
• Opportunities to publish and open-source AI research.
• Work with a high-performance AI supercomputer.
• Combination of startup vitality and job stability.
• Simple, non-corporate culture focused on individual beliefs, learning, growth, and inclusion.