Overview Machine Learning Engineer, AI Platform 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. HealthEdge commits to building an environment and culture that supports the diverse representation of our teams. We aspire to have an inclusive workplace. We aspire to be a place where all employees have the opportunity to belong, make an impact and deliver excellent software and services to our customers. Geographic Responsibility: While HealthEdge is located in Boston, MA you may live anywhere in the US Type of Employment: Full-time, permanent 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. #LI-Remote **The annual US base salary range for this position is $130,000 to $165,000. 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.

• Design, build, and ship AI agents and automation that solve real problems across HealthEdge's engineering, product, and delivery organizations • Develop and implement AI Agents and automation to accelerate internal engineering workflows • Partner with software engineers to integrate AI into the company's existing software infrastructure • Collaborate with product managers, implementation consultants, and business operations teams to identify pain points and scope solutions • Stay current with advancements in LLMs and healthcare technology and apply new knowledge to contribute ideas for innovation • Optimize AI systems for accuracy, latency, cost, and safety • Maintain clear documentation of model development processes, methodologies, and results

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team Stripe processes over $1T in payments volume per year, which is roughly 1% of the world’s GDP. The tremendous amount of data makes Stripe one of the best places to do machine learning. The ML Infra team builds services and tools that power every step in the ML lifecycle, including data exploration, feature generation, experimentation, training, deploying, serving ML models, and building LLM applications. With the phenomenal developments happening in the field of AI, we are positioned to accelerate the adoption of AI/ML across all parts of the company by building highly scalable and reliable foundational infrastructure. What you’ll do You will work closely with machine learning engineers, data scientists, and product engineering teams to enable seamless end-to-end experience in building solutions across data, analytics, and AI/ML platforms. You will build the next generation of ML Infra services and major new capabilities that substantially improve ML development velocity and MLOps maturity across the company. Responsibilities - Designing and building scalable, reliable, and secure services for notebooks, ML model training, experimentation, serving, and LLM applications across multiple regions. - Creating services and libraries that enable ML engineers at Stripe to seamlessly transition from experimentation to production across Stripe’s systems. - Working directly with product teams and ML engineers to improve their day-to-day productivity. - Taking ownership of and finding solutions for technical and product challenges by working with a diverse set of systems, processes, and technologies. Who you are We’re looking for people with a strong background or interest in building successful products or systems; you’re passionate about solving business problems and making impact, you are comfortable in dealing with lots of moving pieces; and you’re comfortable learning new technologies and systems. You are comfortable working with other Stripe teams across the US and Canada. Minimum requirements - 2+ years of professional software development experience with a solid background on service oriented architecture and large-scale distributed systems - Experience working through the full life cycle of software development, from talking to users, to design and implementation, to testing and deployment, to operations - Experience working on production ML platforms, MLOps solutions, or building LLM applications - Experience running operations for high availability, low latency systems - Experience partnering with other teams to drive business outcomes - A sense of pragmatism: you know when to aim for the ideal solution and when to adjust course Preferred qualifications - Experience building and shipping production AI agents - Familiarity with the LLMs and LLM Frameworks - Experience training and shipping machine learning models to production to solve critical business problems

• Architect, design, and implement a production-grade lane and route network mapping stack, ensuring high-performance integration with the broader autonomy system. • Lead the research, design, and training of advanced neural architectures. This includes vectorized mapping networks (e.g., MapTR), multi-camera BEV transformers, and LiDAR-camera fusion models to extract and model lane and route networks for offline and online mapping. • Lead major feature development from inception to deployment. This includes high-level architecture design, rigorous code reviews, automated testing, and technical resolution. • Own the end-to-end data strategy for the mapping domain. You will define data curation, auto-labeling, synthetic data, and active learning pipelines to capture and resolve long-tail scenarios. • Develop robust metrics and evaluation frameworks for lane and route network accuracy, temporal consistency, and scaling across diverse Operational Design Domains (ODDs). • Work independently with cross-functional teams to translate complex autonomy goals into clear software and system requirements. • Collaborate with ML and Autonomy engineers to ensure the seamless deployment and validation of mapping features to the vehicle fleet. • Stay at the research frontier by evaluating, adapting, and innovating cutting-edge techniques. This includes online vectorized HD map construction, end-to-end mapping models, and vision/fusion foundation models to deliver production-ready solutions.