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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.
