Model Optimization Engineer

About the role

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. The position is 100% Remote (U.S.), full-time, direct W2. Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply; the company is unable to sponsor new H-1B visa petitions for this role.

Responsibilities

• Focus on extracting maximum throughput, minimizing latency, and reducing cost across training and inference workloads for large neural network systems.
• Span 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.
• Work closely with cross‑functional partners (product, design, engineering, operations, business stakeholders) to translate ambiguous requirements into well‑engineered solutions.
• Raise the bar through code review, design review, and mentorship of more junior engineers.

Requirements

• 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.

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.

Pay

Salary range: $150,000–$175,000 annually.