--- title: Model Optimization Engineer description: Bright Vision Technologies is hiring for the Model Optimization Engineer role. Pays $150K–$175K per year. See the full description and apply. type: job url: https://www.foundrole.com/jobs/model-optimization-engineer-at-bright-vision-technologies-01a0b3d6-8a2a-7372-8bf7-01f97fc59b4f date: 2026-09-28T04:23:25Z og_description: Join Bright Vision Technologies as Model Optimization Engineer. Pays $150K–$175K per year. Full-time, remote role. og_image: https://www.foundrole.com/og/1uwdvn.png breadcrumbs: - label: Home url: https://www.foundrole.com/ - label: Search url: https://www.foundrole.com/jobs --- | | | |---|---| | **Company** | Bright Vision Technologies | | **Location** | Remote | | **Salary** | $150K/yr - $175K/yr | | **Type** | Full Time | | **Posted** | Sep 28, 2026 | ## Remote check **Fully remote: passed our check.** The role itself is remote, with no recurring office days, travel under 10%, no on-site duties and no end date on the remote setup. - "Location: 100% Remote (U.S.)" **Where you can work from:** United States ## Description 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:** Model Optimization Engineer **Location:** 100% Remote (U.S.) **Position Type:** Full-time, Direct W2 **Salary Range:** $150,000–$175,000 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. **Job Summary:** We are seeking an Model 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. **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 Harry@bvteck.com or contact us at (908)676-4399. Learn more about Bright Vision Technologies at [www.bvteck.com](http://www.bvteck.com). 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. ## Skills - Distributed Systems - High-Performance Computing - Debugging - DeepSpeed - CPU/Processor Architecture - Deep Learning - NVIDIA Triton Inference Server - Code Review - Python - Linux Kernel Development - Artificial Intelligence - LLM Inference - Performance Engineering - Collaboration - Compiler Design - TensorRT-LLM - Model Parallelism - Mentoring - Distributed Training (Machine Learning) - C++ - GPU (Graphics Processing Unit) - Large Language Models (Llm) - Cloud Financial Management (FinOps) - Computer Engineering - Kernel Optimization - Performance Profiling - Design Review - Computer Science - Model Optimization - GPU Architecture - Communication - Machine Learning - Memory Management - vLLM - Model Compression ## How to Apply [Apply for this position](https://www.foundrole.com/jobs/model-optimization-engineer-at-bright-vision-technologies-01a0b3d6-8a2a-7372-8bf7-01f97fc59b4f?utm_source=ai_markdown) ## Related - [Browse all companies](https://www.foundrole.com/companies?utm_source=ai_markdown)