Model Optimization Engineer

Bright Vision Technologies • Frisco, TX

17h • $150,000 - $175,000 • Remote

About The Position

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. The company describes this as an opportunity to join an established organization with career growth potential.

The Model Optimization Engineer focuses on maximizing throughput, minimizing latency, and reducing cost across training and inference workloads for large neural network systems. The work spans low-level kernel optimization through distributed system tuning and calls for understanding GPU architecture, model parallelism, memory management, and compiler-level optimization. The role seeks someone with demonstrated impact on production AI workloads and disciplined instrumentation and measurement to support data-driven optimization decisions.

The engineer works with product, design, engineering, operations, and business stakeholders to translate ambiguous requirements into well-engineered solutions. The position also includes raising engineering standards through code and design reviews and mentoring more junior engineers. The successful candidate is expected to bring strong engineering discipline, clear communication, and a record of delivering robust production work.

This is a full-time, direct W2 position that is 100% remote within the United States. The stated annual salary range is $150,000–$175,000. Applicants may include U.S. citizens, Green Card holders, EAD holders, and candidates transferring an H-1B; the company cannot sponsor new H-1B visa petitions for this role.

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.

Nice To Haves

• 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.

Responsibilities

• Optimize training and inference workloads for large neural network systems to maximize throughput, minimize latency, and reduce cost.
• Improve performance across the stack, from low-level kernels to distributed systems.
• Use instrumentation, profiling, and measurement to guide rigorous, data-driven optimization decisions.
• Collaborate with product, design, engineering, operations, and business stakeholders to turn ambiguous requirements into well-engineered solutions.
• Participate in code reviews and design reviews to raise engineering standards.
• Mentor more junior engineers.
• Deliver meaningful work that performs reliably in production.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior