Boston Human Capital Partners Inc
Machine Learning Engineer
Boston Human Capital Partners Inc, Cambridge, Massachusetts, us, 02140
An AI startup is seeking an experienced
Machine Learning (ML) Engineer
to help advance our mission of revolutionizing healthcare. With an initial focus on breast cancer screening, we will build mammography‑based machine learning (ML) solutions that accurately predict the risk of cancer, personalize the plan of care, cultivate trust, and save lives. We want to expand our team with someone who shares our appreciation for the rapidly evolving power of big data and machine learning, and who enjoys bringing state‑of‑the‑art algorithms to production to solve novel real‑world healthcare problems.
The ML engineer will be responsible for prototyping and deployment of state‑of‑the‑art ML solutions as well as improving the Company’s ML development pipeline. He or she will also assist in collecting, cleaning, and organizing large databases from heterogeneous data sources. The ML engineer will work closely with research partners, data partners, data engineers, ML engineers, and software engineers with the goal of developing commercial‑grade ML solutions.
The ideal candidate will have strong experience in developing, optimizing, and producing machine learning models in medical imaging. He or she will have a strong understanding of ML best development practices and prior experience of data engineering work for ML‑based products. The ideal candidate is a team player, highly motivated self‑starter, detail‑oriented with demonstrated ownership, accountability, and a commitment to high‑quality deliverables.
The location of the position is flexible within the United States, with the ability to work remotely from home.
Primary Responsibilities
Develop state‑of‑the‑art computer vision models for breast cancer risk prediction.
Continuously improve Company’s ML development pipeline.
Assist team with data collection and infrastructure work.
Conduct model validation in collaboration with academic and clinical partners.
Provide engineering assistance for regulatory submissions.
Write publications in peer‑reviewed literature and generate intellectual property materials.
Requirements (Essential)
5+ years of ML development work in a corporate setting in a fast‑paced environment.
5+ years of developing computer vision ML models (deep learning, image processing, large vision models) for image analysis, image segmentation, and image classification tasks.
Demonstrated innovations in ML and/or software as a medical device using ML.
Practical experience of the following technologies:
ML architectures : CNN, Vision Transformers, large vision models
ML toolkits : TensorFlow, Keras, scikit‑learn, PyTorch
Cloud : ML managed services, preferably on AWS
ML : TensorFlow, SageMaker Pipelines
Databases : data warehouse and relational databases
Deployment : Docker containers, AWS CodePipeline
Pipeline orchestration : AWS Step Functions, Airflow, MLFlow
Application exchange : REST API, JSON
Programming languages : Python
Software tools : Git, GitHub, JIRA, Confluence
Requirements (Preferred)
Corporate experience in the regulated medical imaging or healthcare IT industry.
Prior experience implementing orchestration and data management workflow solutions.
Understanding of software development life cycle for medical device software.
Experience in version control of ML models (code, data, config, model) and model registries.
Education
Undergraduate degree in Computer Science or Engineering. Master’s degree or PhD in computer science or engineering preferred.
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Machine Learning (ML) Engineer
to help advance our mission of revolutionizing healthcare. With an initial focus on breast cancer screening, we will build mammography‑based machine learning (ML) solutions that accurately predict the risk of cancer, personalize the plan of care, cultivate trust, and save lives. We want to expand our team with someone who shares our appreciation for the rapidly evolving power of big data and machine learning, and who enjoys bringing state‑of‑the‑art algorithms to production to solve novel real‑world healthcare problems.
The ML engineer will be responsible for prototyping and deployment of state‑of‑the‑art ML solutions as well as improving the Company’s ML development pipeline. He or she will also assist in collecting, cleaning, and organizing large databases from heterogeneous data sources. The ML engineer will work closely with research partners, data partners, data engineers, ML engineers, and software engineers with the goal of developing commercial‑grade ML solutions.
The ideal candidate will have strong experience in developing, optimizing, and producing machine learning models in medical imaging. He or she will have a strong understanding of ML best development practices and prior experience of data engineering work for ML‑based products. The ideal candidate is a team player, highly motivated self‑starter, detail‑oriented with demonstrated ownership, accountability, and a commitment to high‑quality deliverables.
The location of the position is flexible within the United States, with the ability to work remotely from home.
Primary Responsibilities
Develop state‑of‑the‑art computer vision models for breast cancer risk prediction.
Continuously improve Company’s ML development pipeline.
Assist team with data collection and infrastructure work.
Conduct model validation in collaboration with academic and clinical partners.
Provide engineering assistance for regulatory submissions.
Write publications in peer‑reviewed literature and generate intellectual property materials.
Requirements (Essential)
5+ years of ML development work in a corporate setting in a fast‑paced environment.
5+ years of developing computer vision ML models (deep learning, image processing, large vision models) for image analysis, image segmentation, and image classification tasks.
Demonstrated innovations in ML and/or software as a medical device using ML.
Practical experience of the following technologies:
ML architectures : CNN, Vision Transformers, large vision models
ML toolkits : TensorFlow, Keras, scikit‑learn, PyTorch
Cloud : ML managed services, preferably on AWS
ML : TensorFlow, SageMaker Pipelines
Databases : data warehouse and relational databases
Deployment : Docker containers, AWS CodePipeline
Pipeline orchestration : AWS Step Functions, Airflow, MLFlow
Application exchange : REST API, JSON
Programming languages : Python
Software tools : Git, GitHub, JIRA, Confluence
Requirements (Preferred)
Corporate experience in the regulated medical imaging or healthcare IT industry.
Prior experience implementing orchestration and data management workflow solutions.
Understanding of software development life cycle for medical device software.
Experience in version control of ML models (code, data, config, model) and model registries.
Education
Undergraduate degree in Computer Science or Engineering. Master’s degree or PhD in computer science or engineering preferred.
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