Machine Learning Operations Engineer

AccessHope ● Remote

At AccessHope, we’re Fighting cancer with everything we know™ by putting the ever-growing body of cancer knowledge to work for the greater good. Through a revolutionary employer health benefit offering, we remotely connect employees with cancer support services from National Cancer Institute (NCI)–Designated Comprehensive Cancer Centers. Instead of requiring those who have been diagnosed with cancer to come to the centers for renowned cancer expertise, AccessHope brings their support to the patient and their local oncologist—wherever they’re located—to improve care, outcomes, and value.

Hopeful for those we support, rebellious in our approach, and collaborative by breaking down barriers, AccessHope is seeking a Machine Learning Operations Engineer to help us uniquely deploy the latest cancer knowledge to the places it’s needed most. As an ideal candidate, you’ll be responsible for the end-to-end operationalization of machine learning models, ensuring their seamless integration into production environments. You will collaborate with data scientists, software engineers, and other stakeholders to deploy, monitor, and maintain machine learning systems.

Key Responsibilities

• Deploy machine learning models using containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes).
• Ensure smooth integration of models into existing production environments.
• Collaborate with cross-functional teams to design and maintain robust and scalable machine learning infrastructure.
• Optimize system performance and resource utilization.
• Develop and implement automation scripts for model deployment, scaling, and monitoring.
• Manage version control for machine learning models, ensuring reproducibility and traceability.
• Identify opportunities for process improvement and implement best practices for model versioning and documentation.
• Establish comprehensive monitoring solutions for tracking model performance, system health, and potential anomalies.

Required Qualifications

• Bachelor’s degree Computer Science or related technical field. 4 years of experience plus min experience may substitute for minimum education requirements.
• 5+ years of experience in a role related to machine learning operations, DevOps, or software engineering.

Preferred Qualifications

• Master’s degree in Computer Science or a technical related field.
• DevOps or cloud platforms certifications/licensure
• Strong proficiency in scripting and programming languages (e.g., Python).
• Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
• Familiarity with cloud platforms (e.g., AWS, Azure).
• Knowledge of DevOps practices and tools.
• Understanding of machine learning concepts and model development.

Additional Information

• Virtual within the Continental U.S.; support or collaboration across multiple time zones; may include travel up to 25%
• As a condition of employment, AccessHope requires staff to comply with all state and federal vaccination mandates.
• The estimated pay scale represents the typical pay range AccessHope reasonably expects to pay for this position, with offers determined based on several factors which may include, but not be limited to, the candidate’s experience, expertise, skills, education, job scope, training, internal equity, geography/market, etc. This pay scale applies to the current posting only.

AccessHope is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, sex, sexual orientation, identity, age, status as a protected veteran, or status as a qualified individual with disability.