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 full-time, direct W2 position offering a salary range of $150,000–$175,000 annually. U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply; new H-1B visa petitions cannot be sponsored for this role.

We are seeking a 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.

Responsibilities

• Optimize training and inference workloads for large neural network systems to maximize throughput, minimize latency, and reduce cost.
• Work across the full stack from low-level kernel optimization to distributed system tuning.
• Apply deep understanding of GPU architecture, model parallelism, memory management, and compiler-level optimization.
• Use profiling tools across CPU, GPU, and distributed systems to identify bottlenecks and validate improvements.
• Collaborate with cross-functional partners to translate ambiguous requirements into well-engineered solutions.
• Drive code reviews, design reviews, and mentorship of junior engineers to elevate team practices.

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.

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.