Remote AI Systems Performance Engineer

United States Digital Space LLC; United States; Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking a Model Optimization Engineer to push throughput, reduce latency, and cut costs across training and inference workloads for large neural network systems.

The role spans from low‑level kernel optimization to distributed system tuning, requiring deep GPU architecture knowledge, model parallelism, and compiler‑level optimizations, with a focus on delivering measurable end‑to‑end improvements.

Qualifications

• 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.

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 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.

Skills

Python

C++

GPU optimization

Distributed training

Profiling

Model compression

Memory hierarchies

Debugging/analysis

Communication

Tools

Triton

XLA

TorchInductor

TVM

CUTLASS

Job description

Bright Vision Technologies is seeking a Model Optimization Engineer to push throughput, reduce latency, and cut costs across training and inference workloads for large neural network systems.

The role spans from low‑level kernel optimization to distributed system tuning, requiring deep GPU architecture knowledge, model parallelism, and compiler‑level optimizations, with a focus on delivering measurable end‑to‑end improvements.