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• Pioneer Model Development & Optimization: The Machine Learning Engineer will be at the forefront, meticulously evaluating and re-engineering state-of-the-art AI models across the entire spectrum of imaging. This includes developing solutions for de novo protein design, structure identification, and dynamics in single-particle CryoEM, as well as integrating light microscopy and multi-omics data for cross-domain mapping of in situ and in vivo collected data. Your deep technical acumen will transform complex algorithms into practical, high-impact tools.
• Architect Scalable Distributed Systems: Leverage deep software engineering skills to design, develop, and implement reliable, performant, and inherently scalable distributed systems within a dynamic cloud environment. The solutions proposed will form the backbone of our computational infrastructure, handling massive, intricate datasets with precision and efficiency.
• Optimize Data Pipelining for Exascale Training: Take ownership of developing highly efficient data loading strategies and robust performance tracking mechanisms essential for training colossal models. This involves expertly orchestrating distributed training across multiple compute nodes, pushing the boundaries of what's possible in large-scale machine learning.
• Forge Integrated Analysis Pipelines: Engineer, deploy, and meticulously manage complex multi-modal analysis pipelines that serve as the bedrock for scientific analysis and sophisticated machine learning workflows. The vision is to culminate in a unified, intuitively usable framework that empowers our scientists.
• Bridge the Technical and Scientific Divide: Serve as the essential communication conduit, adeptly translating complex technical concepts between experimental scientists, advanced algorithm developers, and deployment engineers. Your ability to foster clear, effective communication will ensure seamless integration and successful project execution.
• Drive Technical & Cultural Excellence: Proactive force in designing and championing technical and cultural standards across both scientific and engineering functions. Your leadership will ensure best practices in code quality, collaboration, and continuous innovation.

• BS/MS in Computer Science, Biomedical Engineering, or a closely related quantitative field.
• 2-5 years of direct, hands-on experience in relevant industry and/or academic settings, showcasing your ability to deliver tangible results.
• Mastery of core programming languages critical for large-scale data management and machine learning, including Python, C++, and deep proficiency with frameworks like PyTorch/TensorFlow, and PyTorch Lightning.
• Demonstrable expertise in Machine Learning at scale, with practical experience in Large Language Models, Self-Supervised/Contrastive/Representation Learning for Computer Vision applications, and multi-modal data integration.
• Proven capability in applying rigorous software engineering practices within a scientific or similarly demanding, high-stakes environment.
• A strong, demonstrable track record of hands-on technical leadership and significant scientific contributions, as evidenced by publications or conference presentations.
• An innate enthusiasm to design, implement, and champion technical and cultural standards that elevate our entire scientific and technical ecosystem.

• Prior experience with bioinformatics data processing and analysis, showcasing a relevant domain understanding.
• Expertise in multi-source data integration, solving complex challenges in disparate datasets.
• Practical experience with cloud computing platforms and containerization technologies, enabling scalable deployments.
• Knowledge of genetics and/or human genetics, further enhancing your ability to contribute to our core mission.

• Machine Learning Engineer I : $153,000 - $07,000
• Machine Learning Engineer II : 178,500 - 241,500

• Machine Learning Engineer I : 150,450 - 203,550
• Machine Learning Engineer II : 170,000 - 230,000

• Design, develop, and implement machine learning models for computer vision tasks, including image classification, object detection, segmentation, and tracking.
• Train, evaluate, and optimize deep learning models using large-scale datasets.
• Develop robust data pipelines for image and video processing.
• Collaborate with software engineers to integrate ML models into production systems and applications.
• Stay current with the latest research and advancements in computer vision and machine learning.
• Conduct experiments to assess model performance and identify areas for improvement.
• Develop and maintain code repositories, ensuring code quality and reproducibility.
• Troubleshoot and resolve issues related to ML model deployment and performance.
• Contribute to the technical roadmap and strategy for AI and computer vision initiatives.
• Present technical findings and project updates to internal teams and stakeholders.

• Master's or Ph.D. in Computer Science, Electrical Engineering, or a related field with a specialization in Machine Learning or Computer Vision.
• 3+ years of professional experience in machine learning engineering, with a focus on computer vision.
• Proficiency in Python and deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Experience with computer vision libraries like OpenCV.
• Solid understanding of image processing techniques and algorithms.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and ML deployment strategies.
• Strong analytical and problem-solving skills.
• Excellent communication and teamwork abilities.
• Ability to work effectively in a fast-paced, innovative environment.

• Designing, developing, and implementing advanced computer vision models using deep learning techniques (CNNs, Transformers, etc.).
• Preprocessing and augmenting large image and video datasets for model training.
• Experimenting with various machine learning algorithms and hyperparameters to optimize model performance.
• Implementing and deploying models into production environments, ensuring scalability and efficiency.
• Collaborating with software engineers and product managers to integrate computer vision capabilities into new and existing products.
• Staying current with the latest research and advancements in computer vision and machine learning.
• Developing robust evaluation metrics and methodologies for computer vision systems.
• Writing clean, efficient, and well-documented code in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, OpenCV).
• Troubleshooting and debugging complex issues in model training and inference.
• Contributing to the design and architecture of machine learning pipelines.
• Communicating technical findings and results to both technical and non-technical audiences.
• Mentoring junior engineers and contributing to team knowledge sharing.
• Ensuring responsible AI development and addressing potential biases in models.
• Working with diverse data sources and formats.

We are seeking a skilled Machine Learning Engineer to join our team. In this role, you will design, develop, and deploy machine learning models that solve real-world problems and drive business value. You’ll work closely with data scientists, software engineers, and product teams to build scalable, production-ready AI solutions.

Responsibilities

• Build, train, and deploy machine learning models and algorithms.

• Collect, preprocess, and analyze large datasets for modeling.

• Develop and maintain end-to-end ML pipelines (data ingestion → model training → deployment).

• Monitor and improve model accuracy, performance, and scalability.

• Collaborate with cross-functional teams to integrate ML solutions into products.

• Research and apply the latest ML/AI techniques and tools.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, or related field (Master’s preferred).

• Strong programming skills in Python (plus experience with libraries such as TensorFlow, PyTorch, or scikit-learn).

• Solid understanding of statistics, probability, and algorithms.

• Experience deploying models in production (cloud platforms like AWS, Azure, or GCP preferred).

• Knowledge of data engineering tools, APIs, and version control (Git).

• Excellent problem-solving skills and ability to explain ML concepts to non-technical stakeholders.

What We Offer

• Competitive pay (salary/hourly depending on experience).

• Remote and flexible work options.

• Opportunities for professional growth and certifications.

• Innovative projects in a collaborative environment.

Machine Learning Engineer at Exact Sciences summary:

The Machine Learning Engineer at Exact Sciences applies advanced AI and machine learning techniques to analyze biological, genomic, clinical, and healthcare data to support cancer screening and precision oncology. They collaborate with biostatisticians, bioinformatics scientists, and data scientists to design, implement, and deploy innovative AI solutions that improve cancer detection, prevention, and treatment. This role involves continuous learning, strategic contribution to AI methodologies, and working within an agile framework to accelerate research and clinical insights.

Help us change lives

At Exact Sciences, we’re helping change how the world prevents, detects and guides treatment for cancer. We give patients and clinicians the clarity needed to make confident decisions when they matter most. Join our team to find a purpose-driven career, an inclusive culture, and robust benefits to support your life while you’re working to help others.

Position Overview

The Machine Learning Engineer, with moderate guidance from more experienced engineers, works individually and in collaboration with others on multiple projects which are moderate to complex in scope. This role utilizes working knowledge of advanced artificial intelligence and machine learning algorithms and models to solve problems involving biological, genomic, clinical and healthcare data within a setting of advanced cancer screening and precision oncology. This role is involved at every step of the solution development process, from ideation, design, implementation and deployment. This position contributes to the strategic vision for the application of cutting-edge AI methodologies at Exact Sciences and works as a partner with biostatisticians, bioinformatics scientists, and data scientists to further our goal of helping eradicate cancer by preventing it, detecting it earlier, and guiding personalized treatment.

Essential Duties

Include, but are not limited to, the following:

• Keep up with external and internal advances in AI technology.
• Collaborate with other ML/AI engineers and relevant partners to articulate “AI affordance”: what do the advances in AI technology allow us to do (or do more efficiently or effectively) that we couldn’t do before?
• Work with stakeholders to discover requirements for ML- and AI-based solutions to business problems with a focus on bioinformatics, biostatistics and data engineering applications.
• Collaborate with other ML/AI engineers and relevant partners to contribute to the design of software solutions that utilize state-of-the art artificial intelligence and machine learning techniques.
• Work with other AI and ML engineers and software engineers and contribute to the implementation and deployment of ML and AI solutions.
• Work in an Agile framework focused on iterative, rapid delivery of proof-of-concept solutions.
• Contribute to the execution of an AI strategy at Exact Sciences that empowers the team to leverage both existing AI and ML tools and those developed in-house to accelerate and enhance bioinformatics, biostatistics and data science processes and outcomes.
• Contribute to internal initiatives to build training resources, develop knowledge bases, and educate on advanced AI and ML technologies and best practices for leveraging them.
• Exercise judgment within broadly defined practices and policies in selecting methods and techniques and evaluation criteria for obtaining and interpreting results.
• Ability to clearly communicate, and to explain basic to intermediate topics to audiences of peers.
• Uphold company mission and values through accountability, innovation, integrity, quality, and teamwork.
• Support and comply with the company’s Quality Management System policies and procedures.
• Maintain regular and reliable attendance.
• Ability to act with an inclusion mindset and model these behaviors for the organization.
• Ability to work designated schedule.
• Ability to work on a mobile device, tablet, or in front of a computer screen and/or perform typing for approximately 85% of a typical working day.
• Ability to travel 5% of working time away from work location, may include overnight/weekend travel

Minimum Qualifications

• Ph.D.  in Statistics, Computational Biology, Computer Science, or related quantitative field as outlined in the essential duties, or master’s degree in Statistics, Computational Biology, Computer Science, or related quantitative field as outlined in the essential duties plus 4 years of experience in lieu of a Ph.D.
• Demonstrated knowledge of advanced AI concepts, such as artificial neural networks, deep learning, and reinforcement learning.
• Demonstrated knowledge of artificial intelligence and machine learning techniques within one or more of the following fields: natural language processing, image processing and computer vision, image and pattern recognition.
• Demonstrated knowledge of large language models for generative AI and associated concepts, such as transformer architecture and retrieval augmented generation.
• Programming ability with demonstrated experience in Python and one or more associated machine learning frameworks, such as TensorFlow, PyTorch, or SKLearn.
• Knowledge of open-source AI models.
• Demonstrated ability to perform the essential duties of the position with or without accommodation.
• Authorization to work in the United States without sponsorship.

Preferred Qualifications

• 1+ years of life sciences industry experience working with biological data.
• 1+ years of industry experience in molecular diagnostics, preferably cancer diagnostics.
• Expertise in data mining approaches within healthcare settings generating insight from routinely collected healthcare data.
• Basic knowledge of ML-Ops and processes for managing the versioning and deployment of machine learning models.
• Scientific understanding of cancer biology

Salary Range:

Exact Sciences is proud to offer an employee experience that includes paid time off (including days for vacation, holidays, volunteering, and personal time), paid leave for parents and caregivers, a retirement savings plan, wellness support, and health benefits including medical, prescription drug, dental, and vision coverage. Learn more about our benefits.

Our success relies on the experiences and perspectives of a diverse team, and Exact Sciences fosters a culture where all employees can develop personally and professionally with a sense of respect and belonging. If you require an accommodation, please contact us here.

Not ready to apply? Join our Talent Community to stay updated on the latest news and opportunities at Exact Sciences.

We are an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to disability, protected veteran status, and any other status protected by applicable local, state, or federal law.

To view the Right to Work, E-Verify Employer, and Pay Transparency notices and Federal, Federal Contractor, and State employment law posters, visit our compliance hub. The documents summarize important details of the law and provide key points that you have a right to know.

Keywords:

machine learning, artificial intelligence, bioinformatics, cancer diagnostics, precision oncology, deep learning, data science, healthcare data, Python, TensorFlow, AI deployment

Metric Bio is recruiting on behalf of a San Francisco–based digital health company that is building an AI-powered platform to transform patient care and healthcare delivery. We are seeking a Machine Learning Engineer who is passionate about applying advanced ML techniques to solve complex challenges in healthcare. This is an opportunity to work on a modern stack with a team that values hands-on technical depth, collaboration, and mission-driven impact.

Responsibilities:

• Design, train, and optimize machine learning models for healthcare applications, including natural language processing, patient risk scoring, and workflow automation.
• Develop and maintain production-grade ML pipelines using MLOps tools (MLflow, Kubeflow, SageMaker, or similar).
• Collaborate with software engineers, data scientists, and clinicians to integrate ML models into scalable production systems.
• Contribute hands-on to engineering across the company’s technical stack: Python/Django, Vue, Kubernetes, and GCP.
• Ensure reliability, fairness, and compliance of models with healthcare standards (HIPAA, HITRUST).
• Participate in code reviews, pair programming, and architecture discussions as part of a collaborative engineering culture.

Qualifications:

• 3–6 years of experience as a Machine Learning Engineer, Applied ML Scientist, or related role.
• Strong programming skills in Python and deep familiarity with frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Hands-on experience deploying ML models into production environments.
• Cloud expertise, ideally with GCP; Kubernetes and containerization experience preferred.
• Knowledge of healthcare data formats (FHIR, HL7) or experience with digital health systems strongly preferred.
• Excellent communication and problem-solving skills, with the ability to collaborate across technical and clinical teams.
• Passion for leveraging AI to improve patient outcomes and healthcare delivery.