Machine Learning Engineer

HealthEdge® offers AI-powered operational infrastructure for health insurance companies, guaranteeing an enduring financial edge in an increasingly competitive market. We're experiencing strong market momentum, with a growing number of health plans choosing HealthEdge to modernize their operations and compete more effectively. As we expand, we're investing in the people who power that growth, making this a pivotal moment to join us and shape the future of healthcare technology. Learn more at HealthEdge.com.

As a Machine Learning Engineer, you will design, build, and ship AI agents and automation that solve real problems across HealthEdge's engineering, product, and delivery organizations, including customer-facing operations. You'll partner directly with stakeholders across Engineering, Product, and healthcare professionals to understand their workflows, identify high-leverage opportunities, and deliver working solutions end-to-end. Your growing expertise in machine learning and agentic AI will have a direct impact on how HealthEdge builds software, delivers for customers, and operates at scale.  

Key Responsibilities  

• AI Platform Development: Develop and implement AI Agents and automation that accelerates internal engineering workflows and customer facing delivery processes, owning the full lifecycle from problem discovery, through prototyping, evaluation, hardening, and production deployment. Contribute reusable libraries, prompt templates, tool-use patterns, and evaluation scaffolding back to the AI Platform.  
• Integration: Partner with software engineers to integrate AI into the company's existing software infrastructure, supporting seamless functionality and performance.  
• Collaboration: Work directly with product managers, implementation consultants, engineers, and business operations teams to identify pain points, scope solutions, and iterate toward measurable outcomes. You are the bridge between what AI can do and what the business needs done.  
• Research and Learning: Stay current with advancements in LLMs, agentic frameworks, machine learning,  and healthcare technology, and apply new knowledge to contribute ideas for innovation within the team.  
• Performance and Reliability: Optimize AI systems for accuracy, latency, cost, and safety, with particular attention to human-in-the-loop design and guardrails appropriate for healthcare.  
• Documentation: Maintain clear documentation of model development processes, methodologies, and results to ensure transparency and reproducibility.  

Required Qualifications  

• Education: Master's degree in Computer Science, Machine Learning, Data Science, or a related field. A Bachelor's degree with relevant experience will also be considered.  

• Experience: 2–4 years of experience building and deploying ML or AI systems in production. Experience working directly with non-technical stakeholders or in embedded/consulting-style engineering roles is a strong plus.  

• Technical Skills: Strong proficiency in Python. Experience with LLM APIs, agentic frameworks (LangChain, Strands, etc.), and prompt engineering alongside traditional ML frameworks (PyTorch, scikit-learn, etc.). Solid software engineering fundamentals — version control, testing, CI/CD, and comfort operating across the full development lifecycle.  

• Healthcare Knowledge: Interest in or familiarity with healthcare data, clinical workflows, and regulatory requirements. Experience working with electronic health records (EHR) or other healthcare datasets is a plus but not required.  

• Analytical Skills: Strong problem-solving skills and the ability to work with complex datasets to derive actionable insights.  

• Communication: Excellent verbal and written communication skills, with the ability to explain technical concepts to non-technical stakeholders.  

• Builder Mindset: Energized by turning ideas into working solutions. You balance speed with quality, thrive in ambiguous problem spaces, and pick up new domains quickly.  

• Team Player: Ability to work collaboratively in a cross-functional team environment, accept feedback, and contribute to the success of the team.  

Geographic Responsibility:  Remote, US

Type of Employment: Full-time, permanent 

FLSA Classification (USA Only): Exempt 

Work Environment: The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job:  

• The employee is occasionally required to move around the office. Specific vision abilities required by this job include close vision, color vision, peripheral vision, depth perception, and ability to adjust focus.  

• Work across multiple time zones in a hybrid or remote work environment. 

• Long periods of time sitting and/or standing in front of a computer using video technology. 

• May require travel dependent on company needs. 

The above statements are intended to describe the general nature and level of the job being performed by the individual(s) assigned to this position. They are not intended to be an exhaustive list of all duties, responsibilities, and skills required. HealthEdge reserves the right to modify, add, or remove duties and to assign other duties as necessary. In addition, reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of this position in compliance with the Americans with Disabilities Act of 1990.  Candidates may be required to go through a pre-employment criminal background check. 

HealthEdge is an equal opportunity employer. We are committed to workforce diversity and actively encourage all qualified persons to seek employment with us, including, but not limited to, racial and ethnic minorities, women, veterans and persons with disabilities. 

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**The annual US base salary range for this position is $X to $135,000. This salary range may cover multiple career levels at HealthEdge. Final compensation will be determined during the interview process and is based on a combination of factors including, but not limited to, your skills, experience, qualifications and education.