ML Performance Engineer (AI)

Location: 100% remote within the United States; the posting also lists Bothell, WA.

Salary: $100,000–$150,000 annually.

Company

hirify.global is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.

What you will do

• Profile and optimize end-to-end AI training and inference pipelines for throughput, latency, and cost.
• Identify bottlenecks across data loading, model computation, communication, and memory.
• Implement quantization, sparsity, pruning, KV cache optimization, continuous batching, and speculative decoding.
• Optimize distributed training with tensor parallelism, pipeline parallelism, FSDP, and ZeRO-style sharding.
• Drive compiler and kernel optimizations using Triton, XLA, TorchInductor, or TVM.
• Build benchmark suites and regression frameworks, evaluate new hardware and software, and document performance playbooks.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
• Six or more years of experience in performance engineering, ML systems, or HPC.
• Strong proficiency in Python and C++.
• Hands-on experience optimizing deep learning workloads on modern GPUs.
• Deep understanding of distributed training and inference, profiling, memory hierarchies, communication primitives, and parallelism.
• U.S. work authorization is required; the role is open to U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates.

Nice to have

• Production-scale LLM inference optimization experience.
• Contributions to vLLM, TensorRT-LLM, DeepSpeed, or similar projects.
• Custom kernel authoring with Triton or CUTLASS.
• FinOps experience for AI workloads.
• Publications or talks on AI systems performance.

Culture & Benefits

• Full-time direct W2 employment.
• Collaboration with product, design, engineering, operations, and business stakeholders.
• Opportunities to contribute to production AI systems and broader ML framework improvements.
• Mentorship, code reviews, design reviews, and knowledge sharing across engineering teams.