AI Performance Engineer

Bright Vision Technologies•Monroe Township, MI

1d•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is seeking an AI Performance Optimization Engineer for a 100% remote, full-time, direct W2 position based in the continental United States. The role is part of the company’s in-house, long-term Statement of Work engagement; Bright Vision Technologies is the employer, with no third-party client or vendor arrangement. The position offers a stated salary range of $100,000–$150,000 per year, plus benefits, and requires at least six years of relevant experience. No new H1B sponsorship is available, but qualified candidates needing an H1B transfer are welcome to apply. A technical coding assessment is mandatory. The engineer will improve throughput, latency, and cost across large neural-network training and inference workloads. The work covers the stack from kernel and compiler optimization through distributed-system tuning, and requires a strong grounding in GPU architecture, model parallelism, memory management, and performance measurement. The engineer will partner with product, design, engineering, operations, and business stakeholders to turn ambiguous requirements into production-ready solutions. The role also involves raising engineering standards through code and design reviews, mentoring junior engineers, communicating findings, and delivering work that performs reliably in production. Bright Vision Technologies describes itself as an equal opportunity employer committed to diversity and inclusion and to providing reasonable accommodations for applicants and employees.

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 high-performance computing.
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 understanding of memory hierarchies, communication primitives, and parallelism strategies.
Excellent measurement, debugging, and analytical reasoning skills.
Strong communication and collaboration skills.
Candidates must be willing to work as a full-time direct W2 employee of Bright Vision Technologies; C2C, 1099, and third-party arrangements are not accepted.
A technical coding assessment is mandatory.
No new H1B sponsorship is available; qualified candidates who need an H1B transfer may apply.

Nice To Haves

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

Responsibilities

Profile and optimize end-to-end AI training and inference pipelines for throughput, latency, and cost.
Identify and eliminate bottlenecks in data loading, model computation, communication, and memory use.
Implement and tune quantization, sparsity, and pruning strategies to reduce model size and accelerate inference.
Optimize distributed training using tensor parallelism, pipeline parallelism, FSDP, and ZeRO-style sharding.
Tune attention implementations, including FlashAttention and paged attention.
Implement KV-cache optimization, continuous batching, and speculative decoding for LLM serving.
Drive compiler-level optimization using Triton, XLA, TorchInductor, or TVM, collaborating with the ML framework community on measurable end-to-end improvements.
Optimize data pipelines, sharding strategies, and storage-access patterns for high-throughput training.
Build and maintain benchmark suites and regression frameworks across workloads.
Collaborate with ML and platform engineering teams to embed best practices in standard pipelines.
Improve cost efficiency 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 across engineering teams.
Track AI systems research and translate relevant advances into production improvements.
Work with product, design, engineering, operations, and business stakeholders to translate requirements into solutions.
Contribute through code reviews, design reviews, and mentorship of more junior engineers.