The Role
Optimize end-to-end training and inference for large neural networks by profiling and removing bottlenecks across data, compute, communication, and memory. Implement quantization, sparsity, sharding, attention and KV-cache optimizations, drive compiler/kernel improvements, build benchmarks and regression suites, evaluate hardware, and embed performance best practices into production pipelines.
Summary Generated by Built In
Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. As we conti nue to grow, we’re looking for a skilled AI Performance Optimization Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology.
This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.AI Performance Optimization EngineerJob Title: AI Performance Optimization Engineer
Location: 100% Remote (Continental United States)
Position Type: In-house Bright Vision Technologies SOW engagement (no third-party client or vendor)
Experience: 6+ years
Salary Range : $100k to $150k per annnum
Sponsorship: No new H1B sponsorship available. H1B transfers welcomed for qualified candidates.
Employment Type: Full-time, direct W2 with Bright Vision Technologies (no C2C, no 1099, no third-party)
Engagement: Long-term, multi-year, aligned to the Bright Vision SOW delivery roadmap
Compensation: Competitive base salary commensurate with experience, plus benefits.
Employment Terms & Visa Policy
This is a 100% remote, full-time, direct W2 position with Bright Vision Technologies.
This role is part of Bright Vision Technologies’ in-house Statement of Work (SOW) engagement. The client, end customer, and employer for this position is Bright Vision Technologies — there is no third-party client, vendor, or implementation partner involved.
We do not engage in C2C, 1099, or third-party arrangements for this role.
BUT STRICTLY NO C2C/1099/3RD PARTY COMPANIES. ALL OUR ROLES ARE W2 AND NO 3RD PARTY BROKERING PLEASE.
Candidates must be willing to work directly as a full-time W2 employee of Bright Vision Technologies and contribute to our in-house SOW deliverables.
No new H1B sponsorship is available for this role.
However, candidates who are currently on a valid H1B visa and require a transfer are welcome to apply. We will support H1B transfers for qualified candidates.
For every role, a technical coding assessment is mandatory. Please apply only if you are confident in your technical abilities and hands-on experience.
Job Summary
We are seeking an AI Performance Optimization Engineer to focus on extracting maximum throughput, 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 has demonstrated impact on production AI 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-engineered solutions, and will 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 record of shipping meaningful work that holds up well in production.
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.
- 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.
- 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.
How to Apply
Would you like to know more about this opportunity?
For immediate consideration, please send your resume to [email protected] or contact us at (908) 505-3545. Learn more about Bright Vision Technologies at www.bvteck.com.
We recognize that our people are our strength, and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs.
Bright Vision Technologies is an Equal Opportunity Employer, including Disability/Veterans.
Position offered by “No Fee Agency.”
Skills Required
- Bachelor's or Master's degree in Computer Science, Computer Engineering, or related field
- Six or more years of experience in performance engineering, ML systems, or HPC
- Strong proficiency in Python
- Strong proficiency in 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 (quantization, sparsity, pruning)
- Strong grasp of memory hierarchies, communication primitives, and parallelism strategies
- Excellent measurement, debugging, and analytical reasoning skills
- Strong communication and collaboration skills
- Technical coding assessment (mandatory)
- 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
- Experience driving compiler-level optimizations (Triton, XLA, TorchInductor, TVM)
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The Company
What We Do
Bright Vision Technologies is a minority-owned organization founded in July 2020 and based in New Jersey, USA. The company specializes in delivering top-tier staffing and IT consulting services, including custom computer programming and systems design. Additionally, they are a product engineering firm with a flagship AI-powered talent intelligence and enterprise automation platform called Lumina, which helps transform IT into a strategic asset for their valued partners.
