AI Performance Optimization Engineer

Bright Vision Technologies

AI Performance Optimization Engineer developing and optimizing AI workloads with a focus on throughput and latency. Collaborating across teams to implement solutions for performance gains.

Posted 5/17/2026full-timeRemote • New York • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

Distributed SystemsPython

About the role

Key responsibilities & impact

Profile and optimize end-to-end AI training and inference pipelines for throughput, latency, and cost.
Identify and eliminate bottlenecks across data loading, model compute, communication, and memory.
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.
Tune attention implementations using FlashAttention, paged attention, and related techniques.
Implement KV cache optimization, continuous batching, and speculative decoding for LLM serving.
Drive compiler-level optimizations using Triton, XLA, TorchInductor, or TVM, working with the broader ML framework community to land improvements that translate into measurable end-to-end performance gains.
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 best practices in standard pipelines.
Drive cost-efficiency improvements through model architecture, hardware selection, and scheduling strategies.
Evaluate new hardware and software offerings, and advise on adoption.
Document performance tuning playbooks and share findings broadly across engineering teams.
Stay current with AI systems research and translate advances into production improvements.

Requirements

What you’ll need

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 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 skills.

Benefits

Comp & perks

Comprehensive benefits
Competitive compensation packages
Supportive work-life balance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonC++deep learningquantizationsparsitypruningtensor parallelismpipeline parallelismFlashAttentionprofiling tools

Soft Skills

analytical reasoningmeasurement skillsdebugging skillscommunication skillscollaboration skills