{"id":1774177,"url":"https://alion.io/job/bright-vision-technologies-ml-performance-engineer-bright-vision-technologies-5","title":"ML Performance Engineer - Bright Vision Technologies","company":{"id":452187,"name":"Bright Vision Technologies","domain":"brightvisiontechnologies.com","url":"https://alion.io/company/bright-vision-technologies-2","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"staffing","is_intermediary":false,"listed_via":null,"ats_vendor":"JazzHR","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Plymouth, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":100000,"max":150000,"currency":"USD","period":"year","gross":null,"usd_annual":150000},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache TVM","optional":false},{"name":"C++","optional":false},{"name":"FSDP","optional":false},{"name":"HPC","optional":false},{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"Triton","optional":false},{"name":"XLA","optional":false},{"name":"CUTLASS","optional":true},{"name":"DeepSpeed","optional":true},{"name":"FinOps","optional":true},{"name":"TensorRT","optional":true},{"name":"TensorRT-LLM","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-10-03T16:04:34Z","employer_posted_date":"2026-10-03","last_verified_at":"2026-10-12T23:44:42Z","board_verified":true,"closed_at":null,"days_open":9,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":9},"description":"ML Performance Engineer - Remote\nBright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.\nThis is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.\nJob Title: ML Performance Engineer\nLocation: 100% Remote (U.S.)\nPosition Type: Full-time, Direct W2\nSalary Range: $100,000-$150,000 Annually\nExperience Required: 6+ years\nSponsorship: 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.\nJob Summary\nWe 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.\nKey Responsibilities \nProfile and optimize end-to-end AI training and inference pipelines for throughput, latency, and cost. \nIdentify and eliminate bottlenecks across data loading, model compute, communication, and memory. \nImplement and tune quantization, sparsity, and pruning strategies to reduce model footprint and accelerate inference. \nOptimize distributed training using tensor parallelism, pipeline parallelism, FSDP, and ZeRO-style sharding. \nTune attention implementations using FlashAttention, paged attention, and related techniques. \nImplement KV cache optimization, continuous batching, and speculative decoding for LLM serving. \nDrive 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. \nOptimize data pipelines, sharding strategies, and storage access patterns for high-throughput training. \nBuild and maintain rigorous benchmark suites and regression frameworks across workloads. \nCollaborate with ML and platform engineering teams to embed best practices in standard pipelines. \nDrive cost-efficiency improvements through model architecture, hardware selection, and scheduling strategies. \nEvaluate new hardware and software offerings, and advise on adoption. \nDocument performance tuning playbooks and share findings broadly across engineering teams. \nStay current with AI systems research and translate advances into production improvements. \nRequired QualificationsBachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field. \nSix or more years of experience in performance engineering, ML systems, or HPC. \nStrong proficiency in Python and C++. \nHands-on experience optimizing deep learning workloads on modern GPUs. \nDeep understanding of distributed training and inference techniques. \nExperience with profiling tools across CPU, GPU, and distributed systems. \nFamiliarity with model compression techniques and their accuracy implications. \nStrong grasp of memory hierarchies, communication primitives, and parallelism strategies. \nExcellent measurement, debugging, and analytical reasoning skills. \nStrong communication and collaboration skills. \nPreferred QualificationsExperience optimizing LLM inference at production scale. \nContributions to vLLM, TensorRT-LLM, DeepSpeed, or similar projects. \nFamiliarity with custom kernel authoring in Triton or CUTLASS. \nExperience with FinOps for AI workloads. \nPublications or talks on AI systems performance. \n How to ApplyWould you like to know more about this opportunity? For immediate consideration, please send your resume to  or contact us at (908) 505-3544. Learn more about Bright Vision Technologies at www.bvteck.com.\nBright Vision Technologies is an Equal Opportunity Employer.\nEqual Employment Opportunity (EEO) Statement\nBright 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.\nBV Teck expressly prohibits any form of workplace harassment or discrimination. 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