Offer summary

Qualifications:

Strong experience in computer vision ML, including semantic segmentation and deep learning architectures., Strong data science skills, including data preprocessing, visualization, and feature engineering., Proficiency in PyTorch, with experience in transformer-based vision models., Knowledge of Airflow, Kubeflow, and scalable ML workflows..

Key responsabilities:

Develop and optimize computer vision models in PyTorch.

Conduct data analysis to understand dataset biases and performance bottlenecks.

Build scalable ML pipelines with Airflow and Kubeflow.

Deploy and monitor ML models on AWS and Kubernetes-based GPU clusters.

Job description

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Machine Learning Engineer – Data Science Focus (Computer Vision in Agrotech)

We are looking for an ML Engineer with a Data Science focus to work at the intersection of ML modeling and data analysis, 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.

This role involves developing CV models while also analyzing datasets, optimizing labeling strategies, and implementing data-driven model improvements. You will work with PyTorch-based CV models, ResNet and Vision Transformer architectures, and ML pipelines in an AWS and on-prem GPU environment.

Key Responsibilities

Develop and optimize computer vision models (semantic segmentation, object detection) in PyTorch.
Conduct data analysis to understand dataset biases, distribution shifts, and performance bottlenecks.
Experiment with Vision Transformers and self-supervised learning techniques for improved model performance.
Build scalable ML pipelines with Airflow and Kubeflow.
Optimize dataset creation and labeling workflows using active learning strategies.
Deploy and monitor ML models on AWS and Kubernetes-based GPU clusters.
Work closely with agronomists and domain experts to interpret model results and refine datasets.

Requirements

Strong experience in computer vision ML, including semantic segmentation, and deep learning architectures.
Strong data science skills, including data preprocessing, visualization, and feature engineering.
Proficiency in PyTorch, with experience in transformer-based vision models.
Knowledge of Airflow, Kubeflow, and scalable ML workflows.
Familiarity with Kubernetes, Slurm, and cloud/on-prem GPU deployments.
Strong analytical and problem-solving skills.
Ability to work cross-functionally with domain experts and software engineers.

Nice-to-Haves

Experience with unsupervised and self-supervised learning methods.
Exposure to active learning, dataset curation, and annotation tooling.
Background in agriculture technology or geospatial ML is a plus but not required.

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