Senior ML Performance Engineer

well-funded deeptech startup; California (MO); On-site; USD 200,000 - 250,000; Full time

Job summary

Join a well-funded deeptech startup in California as a Senior ML Performance Engineer. You will lead the optimization of ML inference pipelines and improve model performance for innovative clients.

This role requires strong skills in Python, TensorFlow, and PyTorch, as well as expertise in performance optimization techniques. The ideal candidate is eager to work on cutting-edge ML projects in an energetic environment with potential for significant impact.

Qualifications

• Strong proficiency in Python and ML frameworks such as TensorFlow and PyTorch.
• Deep understanding of ML algorithms and architectures.
• Expertise in performance optimization techniques including profiling and quantization.

Responsibilities

• Conduct in-depth performance profiling and analysis of ML models.
• Design and implement efficient ML inference pipelines.
• Collaborate with infrastructure teams to optimize configurations.

Skills

Python

TensorFlow

PyTorch

Performance optimization techniques

Kubernetes

AWS

GCP

Azure

Job description

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.