AI Performance Engineer

Bright Vision Technologies; Cranberry Township (Butler County); Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an AI Performance Engineer for a 100% remote U.S. role. You will profile and optimize end-to-end AI training and inference pipelines, focusing on throughput, latency, and cost reductions across data loading, compute, and memory.

The role requires 6+ years in performance engineering and expertise in Python/C++, distributed training, and GPU optimizations. Collaboration with ML and platform teams is essential to deploy scalable improvements.

Qualifications

• 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.

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++

Performance engineering

ML systems

HPC

Profiling tools

Distributed training

GPU optimization

Memory hierarchies

Debugging

Collaboration

Education

Bachelor’s or Master’s degree in Computer Science or Computer Engineering

Tools

Triton

XLA

TorchInductor

TVM

CUTLASS

TensorRT-LLM

DeepSpeed

Job description

AI 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: AI 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, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.

Key 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

Bright Vision Technologies is an Equal Opportunity Employer.

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.