Senior Software Engineer - ML Infrastructure

Claryo, Inc.; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Claryo, Inc. is seeking a Senior Machine Learning Engineer to build and scale the infrastructure powering our AI-driven warehouse intelligence platform.

You will own the end-to-end lifecycle of ML vision models, from training pipelines to optimized cloud deployment, ensuring our computer vision and multi-modal AI systems run reliably in production. You will design scalable GPU compute clusters, orchestration pipelines, and low-latency model serving.

Qualifications

• BS/MS in CS/Robotics or related field, or equivalent practical experience.
• 5+ years of professional software engineering experience.
• 3+ years in ML— developing, scaling, deploying, and optimizing large-scale ML systems from data to model.
• Track record of deploying ML models in production environments.
• Experience with training and/or deployment of ML models in computer vision.
• Strong programming skills in Python.

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 using platforms and inference optimization techniques, including VLMs and VLAs.
• Establish monitoring, benchmarking, and evaluation frameworks to ensure model performance and reliability in production environments.

Skills

Python

Machine learning

Distributed systems

Cloud deployment

Data pipelines

Education

B.S./M.S. in Computer Science or related field

Tools

PyTorch

TensorFlow

DeepSpeed

Ray

TorchServe

TensorFlow Serving

NVIDIA Triton Inference Server

Flyte

Temporal

Airflow

Job description

We're looking for a Senior Machine Learning Engineer to build and scale the infrastructure that powers our AI-driven warehouse intelligence platform. You'll own the end-to-end lifecycle of ML vision models — from training pipelines through optimized cloud deployment — ensuring our cutting-edge computer vision and multi-modal AI systems run reliably and efficiently in production. Your work will directly enable the real-time perception and autonomous decision-making capabilities at the core of our platform.

This is a deeply technical role at the intersection of machine learning, computer vision and distributed systems. You'll design scalable GPU compute clusters, build robust orchestration pipelines, and optimize model serving for low-latency inference at scale. You'll work closely with our research scientists, computer vision engineers, and product teams to bridge the gap between experimental models and production-ready systems that operate across diverse warehouse environments. We've found tremendous value in collaborative problem-solving, thus our team works from our SF office three days a week.

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 using platforms and inference optimization techniques, including VLMs and VLAs.
• Establish monitoring, benchmarking, and evaluation frameworks to ensure model performance and reliability in production environments.

Required Experience

• B.S. / M.S. in Computer Science, Robotics, or similar technical field, or equivalent practical experience.
• 5+ years of professional software engineering experience.
• 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 with training and/or deployment of ML models in the computer vision domain.
• Strong programming skills in Python.

Preferred Experience

• Experience using orchestration systems (Flyte, Temporal, Airflow, or similar) for ML and data pipelines.
• Proficiency with ML frameworks (PyTorch, TensorFlow, DeepSpeed, Ray) and model serving platforms (TorchServe, TensorFlow Serving, NVIDIA Triton Inference Server, or similar).
• Deep understanding of state-of-the-...