Senior Software Engineer - ML Infrastructure

Claryo•San Francisco, CA

1d•$170,000 - $190,000•Hybrid

About The Position

Build and scale the infrastructure powering an AI-driven warehouse intelligence platform. Own the end-to-end lifecycle of machine-learning vision models, from training pipelines through optimized cloud deployment, and ensure computer-vision and multimodal AI systems run reliably and efficiently in production. The work enables real-time perception and autonomous decision-making across diverse warehouse environments. This deeply technical role spans machine learning, computer vision, and distributed systems. It involves designing scalable GPU compute clusters, building orchestration pipelines, and optimizing model serving for low-latency inference at scale. The engineer collaborates with research scientists, computer-vision engineers, and product teams to turn experimental models into production-ready systems. The team works from its San Francisco office three days a week.

Requirements

B.S. or M.S. in Computer Science, Robotics, or a similar technical field, or equivalent practical experience.
At least 5 years of professional software engineering experience.
At least 3 years in machine learning, developing, scaling, deploying, and optimizing large-scale ML systems from data to model.
Track record of deploying machine-learning models in production environments.
Experience training and/or deploying ML models in the computer-vision domain.
Strong programming skills in Python.

Nice To Haves

Experience using orchestration systems such as Flyte, Temporal, Airflow, or similar for ML and data pipelines.
Proficiency with ML frameworks such as PyTorch, TensorFlow, DeepSpeed, or Ray, and model-serving platforms such as TorchServe, TensorFlow Serving, NVIDIA Triton Inference Server, or similar.
Deep understanding of state-of-the-art machine-learning models such as autoregressive transformers and familiarity with inference optimization techniques such as TensorRT, quantization, and custom kernels.
Experience with C++ or CUDA programming for GPU acceleration.
Comfort with distributed messaging and compute systems such as Kafka, gRPC, ROS2, or similar.
Prior experience at autonomous-vehicle or robotics companies.

Responsibilities

Build end-to-end ML pipelines, from data ingestion and preprocessing through model training, evaluation, and deployment, and integrate them into core product workflows.
Train, evaluate, deploy, and optimize state-of-the-art machine-learning models in the cloud, including VLMs and VLAs, using inference optimization techniques.
Establish monitoring, benchmarking, and evaluation frameworks to ensure model performance and reliability in production environments.