Model Optimization Engineer
About The Position
Bright Vision Technologies is seeking a Model Optimization Engineer to improve throughput, reduce latency, and lower costs across training and inference workloads for large neural network systems. The role spans low-level kernel optimization through distributed-system tuning and requires expertise in GPU architecture, model parallelism, memory management, and compiler-level optimization. The engineer will use instrumentation and measurement to guide data-driven optimization decisions for production AI workloads. The position works closely with product, design, engineering, operations, and business stakeholders to turn ambiguous requirements into well-engineered solutions. The engineer is also expected to contribute to code and design reviews and mentor more junior engineers. Bright Vision Technologies describes itself as a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.
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 throughput, latency, and cost across training and inference workloads for large neural network systems.
- Tune performance across low-level kernels and distributed systems.
- Use instrumentation and measurement to make rigorous, data-driven optimization decisions.
- Work with product, design, engineering, operations, and business stakeholders to translate requirements into engineered solutions.
- Contribute to code reviews and design reviews.
- Mentor more junior engineers.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
