Introduction
We are seeking an AI Performance Optimization Expert who excels at improving the efficiency and scalability of machine learning systems. If you are passionate about accelerating AI pipelines, reducing latency, and enabling large-scale deployments, this role provides an opportunity to directly impact performance at the core of advanced AI systems.
As an AI Performance Optimization Expert at our organization, you will analyze bottlenecks, optimize model architectures, and refine system-level performance to ensure our AI solutions operate at industry-leading speeds and reliability.
We offer competitive compensation, comprehensive benefits, and opportunities to push the limits of high-performance AI at scale.
Objectives of this role
- Optimize machine learning models for inference speed and training efficiency.
- Reduce latency in real-time AI applications and large-scale deployments.
- Collaborate with research and infrastructure teams to ensure performance at scale.
- Develop monitoring tools to track efficiency and resource utilization.
Your tasks
- Profile and analyze model performance across training and inference stages.
- Implement optimizations such as quantization, pruning, and knowledge distillation.
- Deploy models on GPUs, TPUs, and edge hardware with optimized performance.
- Collaborate with infrastructure teams to optimize distributed training pipelines.
- Develop benchmarking frameworks to evaluate system efficiency.
- Provide guidance on performance best practices to ML teams.
Required skills and qualifications
- Bachelor’s degree in Computer Science, Engineering, or related field.
- 4+ years of experience optimizing AI/ML models and systems.
- Proficiency with Python and ML frameworks (TensorFlow, PyTorch, ONNX).
- Knowledge of GPU acceleration, CUDA programming, and distributed training.
- Strong problem-solving and performance profiling skills.
Preferred skills and qualifications
- Experience with model compression and optimization libraries (TensorRT, OpenVINO).
- Knowledge of edge AI deployment and hardware-aware model design.
- Familiarity with large-scale data pipelines and HPC.
- Experience in real-time AI applications such as NLP or computer vision.
- Contributions to open-source optimization tools or performance benchmarks.
