Staff Machine Learning Engineer

Role overview Own the design and operation of machine learning systems across data processing, training, evaluation, inference, and deployment. This senior technical role combines hands-on work on large models and GPU infrastructure with architectural decision-making and technical leadership for other ML engineers.

Responsibilities - Design ML systems and production workflows from dataset preparation through model deployment. - Build training and fine-tuning pipelines for large models, and frameworks to evaluate quality and reliability. - Develop processes for collecting, processing, and validating training and evaluation data. - Improve inference performance, including latency, throughput, GPU use, and operating cost. - Deploy, monitor, and troubleshoot models and supporting infrastructure in production. - Set system architecture, review designs and code, and resolve complex production problems. - Guide ML engineers and collaborate with research and application teams on production performance.

Requirements - At least 8 years of software or ML engineering experience, including production ML systems. - Strong Python skills and hands-on experience with PyTorch or JAX. - Experience training, fine-tuning, and evaluating large machine learning models. - Experience building and operating GPU-based training or inference systems. - Strong understanding of distributed computing, data pipelines, and ML infrastructure. - Track record optimizing model performance, inference latency, and resource use. - Ability to design ML architecture, review code, handle complex incidents, and provide technical leadership.

Nice to have - Experience with large language models, transformers, or distributed model training. - Familiarity with inference frameworks such as vLLM or TensorRT-LLM. - Experience in synthetic data, data curation, or model evaluation pipelines. - Experience deploying ML systems for consumer products.