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-art machine learning models such as auto-regressive transformers and familiarity with inference optimization techniques (TensorRT, quantization, custom kernels). - Experience with C++ or CUDA programming for GPU acceleration. - Comfortable with distributed messaging and compute systems (Kafka, gRPC, ROS2, or similar). - Prior experience working at autonomous vehicles or robotics companies. Equal Opportunity Statement We’re an equal opportunity employer that values diversity and inclusion. We welcome teammates of all backgrounds and don’t discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status. Benefits At Claryo, we offer a competitive benefits package that supports your health and well-being, including — top-tier medical, dental, and vision coverage, 401k with employer matching, parental leave, and unlimited vacation.

Requirements

Must-haves

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.
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.

Nice-to-haves

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-art machine learning models such as auto-regressive transformers and familiarity with inference optimization techniques (TensorRT, quantization, custom kernels).
Experience with C++ or CUDA programming for GPU acceleration.
Comfortable with distributed messaging and compute systems (Kafka, gRPC, ROS2, or similar).
Prior experience working at autonomous vehicles or robotics companies.

Application prep

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Keywords to mirror

machine learningaimlpython

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Parental Leave