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About the Role
Seeking an Model Optimization Engineer to maximize throughput, minimize latency, and reduce cost for large neural network systems. The role involves full-stack optimization from low-level kernels to distributed systems, requiring deep knowledge of GPU architecture, model parallelism, memory management, and compiler optimization. You will collaborate with cross-functional teams to translate requirements into solutions and mentor junior engineers.
Responsibilities
- Extract maximum throughput for training and inference workloads.
- Minimize latency for training and inference workloads.
- Reduce cost for training and inference workloads.
- Optimize low-level kernels.
- Tune distributed systems.
- Work closely with cross-functional partners 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.
- 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.
Skills
- Python
- C++
- GPU architecture
- Model parallelism
- Memory management
- Compiler-level optimization
- Distributed systems tuning
- Profiling tools
- Model compression techniques
- Memory hierarchies
- Communication primitives
- Parallelism strategies
- Measurement
- Debugging
- Analytical reasoning
- Communication
- Collaboration
- LLM inference optimization
- vLLM
- TensorRT-LLM
- DeepSpeed
- Triton
- CUTLASS
- FinOps for AI workloads
Location
- 100% Remote (U.S.)
Work Type
- Full-time
- Direct W2
Experience Level
- 6+ years
Education Level
- Bachelor’s degree in Computer Science, Computer Engineering, or a related field
- Master’s degree in Computer Science, Computer Engineering, or a related field
Salary/Compensations
- $150,000–$175,000 Annually
About the Company
- 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.
Equal Opportunity
- Bright Vision Technologies is an Equal Opportunity Employer.
- Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws.
- This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.
- BV Teck expressly prohibits any form of workplace harassment or discrimination.
- Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.