Senior ML Performance Engineer Ultra-Fast Inference + Equity

Senior ML Performance Engineer | $200-250K salary + equity | Early-stage ML Infrastructure. 30-50 employees, $50MM+ in funding We’re seeking a seasoned ML Performance Optimization Specialist to spearhead the development and deployment of high-performance, scalable ML inference pipelines for a key early-stage company. You’ll optimize model performance, reduce latency, and maximize throughput for some of the most innovative companies in the world. Key Responsibilities: Model Optimization: Conduct in-depth performance profiling and analysis of ML models, identifying and eliminating bottlenecks. Pipeline Engineering: Design and implement efficient ML inference pipelines, leveraging technologies like TensorFlow Serving, TorchServe, and NVIDIA Triton Inference Server. Infrastructure Optimization: Collaborate with infrastructure teams to optimize hardware and software configurations for optimal ML performance, including GPU acceleration, distributed training, and model quantization. Performance Benchmarking: Develop and maintain rigorous performance benchmarks to measure and track improvements. Experimentation: Explore cutting-edge techniques like model quantization, pruning, and knowledge distillation to further enhance performance. Required Skills and Experience: Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch) Deep understanding of ML algorithms and architectures Expertise in performance optimization techniques (profiling, quantization, pruning, etc.) Hands-on experience with container orchestration platforms (Kubernetes) Proficiency in cloud platforms (AWS, GCP, Azure) Strong problem-solving and analytical skills Preferred Qualifications: Experience with ML hardware acceleration (GPUs, TPUs) Knowledge of distributed training frameworks (Horovod, DDP) Familiarity with MLIR and compiler optimization techniques If you’re passionate about pushing the boundaries of ML performance and eager to work on cutting-edge projects, we encourage you to apply. #J-18808-Ljbffr