AI Performance Engineer

About This Opportunity

We are representing a confidential technology consulting organization seeking an AI Performance Engineer to join their team on a fully remote basis. This role offers the opportunity to work on cutting-edge AI systems optimization with significant impact on production workloads.

Responsibilities

• Profile and optimize end-to-end AI training and inference pipelines for throughput, latency, and cost efficiency
• Identify and eliminate bottlenecks across data loading, model compute, communication, and memory systems
• Implement and tune quantization, sparsity, and pruning strategies to reduce model footprint and accelerate inference
• Optimize distributed training using tensor parallelism, pipeline parallelism, FSDP, and ZeRO-style sharding techniques
• Tune attention implementations using FlashAttention, paged attention, and related optimization techniques
• Implement KV cache optimization, continuous batching, and speculative decoding for LLM serving
• Drive compiler-level optimizations using Triton, XLA, TorchInductor, or TVM
• Optimize data pipelines, sharding strategies, and storage access patterns for high-throughput training
• Build and maintain rigorous benchmark suites and regression frameworks across workloads
• Collaborate with ML and platform engineering teams to embed performance best practices
• Drive cost-efficiency improvements through model architecture, hardware selection, and scheduling strategies
• Evaluate new hardware and software offerings and provide adoption recommendations
• Document performance tuning playbooks and share findings across engineering teams

What We're Looking For

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Computer Engineering, or related field
• 6+ 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 techniques
• Experience with profiling tools across CPU, GPU, and distributed systems
• Familiarity with model compression techniques and their accuracy implications
• Strong grasp of memory hierarchies, communication primitives, and parallelism strategies
• Excellent measurement, debugging, and analytical reasoning skills
• Strong communication and collaboration abilities

Preferred Qualifications:

• Experience optimizing LLM inference at production scale
• Contributions to vLLM, TensorRT-LLM, DeepSpeed, or similar open-source projects
• Familiarity with custom kernel authoring in Triton or CUTLASS
• Experience with FinOps for AI workloads
• Publications or conference talks on AI systems performance

Employment Details:

• Location: 100% Remote (United States)
• Position Type: Full-time, Direct W2
• Visa: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. New H-1B sponsorship is not available for this position.

How We Work with You

As your recruiting partner, our talent team at Get A Job.ai will guide you through the entire process. After you apply through our platform, one of our experienced recruiters will conduct an initial screening to understand your background and ensure alignment with the role. We will then prepare and submit your profile to our client for consideration. Please do not contact the hiring organization directly, as all communication and coordination will be managed through Get A Job.ai to ensure the best candidate experience.

Pay

Pay via Get A Job.ai: $100,000–$150,000 annually

Equal Employment Opportunity
Get A Job.ai is committed to equal employment opportunity for all applicants regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic under applicable law.