ML Performance Engineer

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.

This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Job Title: ML Performance Engineer

Location: 100% Remote (U.S.)

Position Type: Full-time, Direct W2

Salary Range: $100,000–$150,000 Annually

Experience

Required:

6+ years

Sponsorship: U.S. Citizens, Green Card Holders, EADHolders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.

We are seeking anAI Performance Optimization Engineer to focus on extractingmaximumthroughput, minimizing latency, and reducing cost across training and inference workloads for large neural network systems. The role spans the full stack from low-level kernel optimization to distributed system tuning, requiring deep understanding of GPU architecture, model parallelism, memory management, and compiler-level optimization. The ideal candidate hasdemonstratedimpactonproductionAI workloads, with strong instrumentation and measurement discipline that enables rigorous, data-driven optimization decisions.

In this role you will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineeredsolutions, andwill be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track recordof shipping meaningful work that holds up well in production.

• Profile andoptimizeend-to-end AI training and inference pipelines for throughput, latency, and cost.
• Identifyandeliminatebottlenecks 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, andZeRO-style sharding.
• Tune attention implementations using
  
  Flash
  
  Attention, paged attention, and related techniques.
• Implement KV cache optimization, continuous batching, and speculative decoding for LLM serving.
• Drive compiler-level optimizations using Triton, XLA,Torch Inductor, 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 andmaintainrigorous 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, andadvise on adoption.
• Document performance tuning playbooks and share findings broadly across engineering teams.
• Stay current with AI systemsresearchand translate advances into production improvements.

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, ora related field.
• Six or more years of experience in performance engineering, ML systems, or HPC.
• Strongproficiencyin Python and C++.
• Hands-on experienceoptimizingdeep 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…