AI Performance Optimization Engineer at Bright Vision Technologies

Fremont, California, United States -

Full Time

Start Date

Immediate

Expiry Date

28 Aug, 26

Salary

150000.0

Posted On

30 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Python, C++, GPU Architecture, Distributed Training, Model Parallelism, Quantization, Sparsity, Pruning, Triton, XLA, TorchInductor, TVM, FlashAttention, vLLM, TensorRT-LLM, DeepSpeed

Industry

Information Technology & Services

Description

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 continue 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. Job 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) Salary: $100K - $150K Experience: 6+ years 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. 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. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to hilda@bvteck.com 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.” 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.

Responsibilities

Optimize end-to-end AI training and inference pipelines to maximize throughput and minimize latency and cost. Implement advanced techniques like quantization, distributed training, and compiler-level optimizations for large neural network systems.