Offer summary

Qualifications:

Strong experience in computer vision machine learning, particularly in semantic segmentation and deep learning architectures., Proficiency in PyTorch, with experience in Vision Transformers being a plus., Familiarity with Kubeflow, Airflow, and ML pipeline orchestration., Strong Python coding skills and experience with model optimization..

Key responsabilities:

Design, implement, and optimize deep learning models for computer vision tasks.

Train and fine-tune models using PyTorch, experimenting with new architectures.

Develop and maintain ML pipelines with Kubeflow and Airflow.

Collaborate with software engineers to integrate ML models into production systems.

Job description

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Machine Learning Engineer (Computer Vision in Agrotech)

We are looking for a Machine Learning Engineer with expertise in deep learning for image processing, for a remote position at our client working at the forefront of agrotech robotics research and development in Silicon Valley, California. Our client is an innovative startup (backed by a machinery manufacturing giant), applying robotics and Computer Vision Machine Learning to groundbreaking agrotech and construction use-cases for more than 10 years, to implement safer and more sustainable solutions.

In this role, you will develop and optimize computer vision models, scale ML pipelines, and deploy solutions in cloud and on-prem environments. You will work with PyTorch, with both ResNet and Vision Transformer architectures, and optimize models for Kubernetes-based GPU clusters in AWS cloud and in an on-prem GPU cluster.

Key Responsibilities

Design, implement, and optimize deep learning models for CV tasks.
Train and fine-tune models using PyTorch, experimenting with new architectures.
Develop and maintain ML pipelines with Kubeflow and Airflow.
Optimize model inference and deployment on AWS Cloud and on-premise GPU clusters (K8s, Slurm).
Collaborate with software engineers to integrate ML models into production systems.
Continuously improve model efficiency, scalability, and accuracy.

Requirements

Strong experience in computer vision ML, including semantic segmentation, and deep learning architectures.
Proficiency in PyTorch; experience with Vision Transformers is a plus.
Familiarity with Kubeflow, Airflow, and ML pipeline orchestration.
Experience deploying models in AWS cloud and on-premise GPU environments.
Hands-on knowledge of Kubernetes and Slurm for distributed computing.
Strong Python coding skills and experience with ML model optimization.
Excellent problem-solving skills and the ability to work in a fast-paced, applied research environment.

Nice-to-Haves

Experience with model conversion, quantization, pruning, and optimization for edge deployment.
Understanding of active learning strategies and data-centric AI approaches

Benefits

Collaborative and tech-focused environment
Opportunity to work with cutting-edge cloud, data, and ML technologies
Fully remote position with opportunities for occasional travel to California

Required profile