AI Performance Engineer

Location: 100% remote within the United States

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, memory, storage access, and distributed workloads.
• Implement model compression and inference optimizations including quantization, sparsity, pruning, FlashAttention, paged attention, 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-level improvements using Triton, XLA, TorchInductor, TVM, or similar technologies.
• Build benchmark and regression frameworks, evaluate new hardware and software, document tuning practices, and collaborate with ML and platform engineering teams.

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, inference, memory hierarchies, communication primitives, and parallelism strategies.
• Must be eligible to work in the United States; new H-1B visa petitions cannot be sponsored.

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 W-2 employment.
• Career growth opportunities within an established organization.
• Collaboration with ML, platform engineering, and broader engineering teams.
• Opportunity to translate AI systems research into production improvements.