ML Performance Engineer

Bright Vision Technologies; Maple Grove (MN); Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an ML Performance Engineer to optimize AI training and inference across large neural network systems. The role spans from low-level kernel optimization to distributed system tuning, requiring deep GPU architecture knowledge and compiler-level optimization.

You will work with cross-functional partners to translate ambiguous requirements into well-engineered solutions and drive measurable end-to-end performance gains.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
• Six+ 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, with broader ML framework community.
• 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 teams.
• Stay current with AI systems research and translate advances into production improvements.

Skills

Python

C++

Performance engineering

Distributed training

Profiling tools

Measurement & debugging

Education

Bachelor’s or Master’s in CS/CE or related field

Tools

Triton

XLA

TorchInductor

TVM

CUTLASS

TensorRT-LLM

Job description

ML Performance Engineer - Remote

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 CardHolders, EADHolders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.

Job Summary

We are seeking anAI Performance Optimization Engineerto 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, anda track recordof shipping meaningful work that holds up well in production.

Key Responsibilities

• 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.
• Optimizedistributed training using tensor parallelism, pipeline parallelism, FSDP, andZeRO-style sharding.
• Tune attention implementations usingFlashAttention, 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.
• Optimizedata 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 softwareofferings, andadvise on adoption.
• Document performance tuning playbooks and share findings broadly across engineering teams.
• Stay current with AI systemsresearchand translate advances into production improvements.

Required Qualifications

• Bachelor’s orMaster’s degree in Computer Science, Computer Engineering, ora relatedfield.
• 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 implications.
• Strong grasp of memory hierarchies, communication primitives, and parallelism strategies.
• Excellent measurement, debugging, and analytical reasoning skills.
• Strongcommunicationand collaboration skills.

Preferred Qualifications

• ExperienceoptimizingLLM inference at production scale.
• Contributions tovLLM,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.

Equal Employment Opportunity (EEO) Statement

Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.

BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.