ML Performance Engineer

Bright Vision Technologies•Highlands Ranch, CO

2d•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is seeking an AI Performance Optimization Engineer to improve throughput, reduce latency, and lower costs across training and inference workloads for large neural network systems. The role spans low-level kernel optimization through distributed-system tuning and calls for expertise in GPU architecture, model parallelism, memory management, and compiler-level optimization. The successful candidate will have demonstrated impact on production AI workloads and use rigorous instrumentation and measurement to guide optimization decisions. The engineer will work with product, design, engineering, operations, and business stakeholders to turn ambiguous requirements into well-engineered solutions. They will contribute through code and design reviews and mentor more junior engineers. The role is full-time, direct W2, and 100% remote within the United States. The stated annual salary range is $100,000–$150,000.

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 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.

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 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 produce 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 across engineering teams.
Stay current with AI systems research and translate advances into production improvements.
Contribute through code review, design review, and mentorship of more junior engineers.