Model Optimization Engineer

Bright Vision Technologies•Overland Park, KS

1d•$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 will maximize throughput, minimize latency, and reduce costs across training and inference workloads for large neural network systems. The role spans low-level kernel optimization and distributed system tuning, drawing on GPU architecture, model parallelism, memory management, and compiler-level optimization. The engineer will apply rigorous instrumentation and measurement to make data-driven optimization decisions for production AI workloads. The role works with product, design, engineering, operations, and business stakeholders to turn ambiguous requirements into well-engineered solutions. It also includes raising engineering standards through code and design reviews and mentoring more junior engineers. The successful candidate will bring engineering discipline, clear communication, and a record of delivering meaningful, production-ready work.

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.
Tune performance across the stack, from low-level kernels to distributed systems.
Use instrumentation, profiling, and measurement to make rigorous, data-driven optimization decisions.
Collaborate with product, design, engineering, operations, and business stakeholders to translate ambiguous requirements into well-engineered solutions.
Conduct code and design reviews and mentor more junior engineers.