Senior Machine Learning Engineer (Remote, USA)

At RXNT, we see ourselves as more than a healthtech company—we’re the digital backbone of the U.S. healthcare system. Every day, our platforms empower healthcare professionals to deliver better patient care, streamline medication and lab ordering, simplify billing and insurance processes, and strengthen connections across the entire healthcare ecosystem. For over 25 years, we’ve been innovating at the intersection of healthcare and technology. That history has taught us a profound truth: transforming healthcare is both a privilege and a responsibility. It requires us to uphold the highest standards, think with rigor, and move with urgency when patients’ lives are on the line. As we expand our AI-driven product offerings, we’re tackling some of the most exciting and complex challenges in machine learning and healthcare. Saving the healthcare providers hours of after-hours work and burnout, providing the means for a patient-focused care, and improving the care accuracy and reliability are but a few of key values we are offering the health system today using AI. We’re looking for curious, self-motivated Senior Machine Learning Engineers who thrive in fast-moving environments, embrace ownership, and never stop learning. You’ll join a team that combines the energy of a startup with the stability and trust of a company that has spent decades shaping healthcare technology. Led by seasoned entrepreneurs and technologists, you’ll have the opportunity to push boundaries, solve meaningful problems, and help define the future of AI in healthcare. We're looking for an experienced MLE: Has a proven track record of delivering mission-critical models deployed at scale. Have strong foundational understanding of machine learning disciplines, advanced ML techniques, and advanced prompt engineering. Understands the full ML lifecycle —from data analysis to model fine-tuning, evaluations and deployment, you will own the end-to-end development of advanced models. Productionize models —strong hands-on production skills. Is excited about orchestrating (self-improving) AI agents and fine-tuning, evaluating and deploying models in production environments. Key Responsibilities Key Responsibilities: 1. Model Fine-Tuning Fine-tune, and optimize various multi-modal generative models, for various use cases. Experiment with techniques to enhance performance while keeping a production-first mindset. 2. Generative AI Orchestration Build and manage ML graphs/workflows using orchestration frameworks like LangGraph or similar tools to enable AI-driven agents. 3. Building for Production Write robust Python code that meets high standards and familiarity with software engineering principles. Collaborate with software engineers to integrate the ML models into larger, distributed systems. Adhere to best practices in code reviews, version control, CI/CD, and testing. 4. ML Pipeline Management While MLOps is not your primary role, you should have a strong foundation in full ML lifecycle and be familiar with cloud platforms (AWS, GCP, Azure), containerization, and monitoring to ensure models run reliably in production. 5. Collaboration & Leadership Work closely with data teams, software engineers, and product teams to design and deliver impactful AI features. Champion best practices in machine learning. Contribute to the direction of the company’s AI strategy. Skills, Knowledge and Expertise Skills, Knowledge and Expertise: 1. Professional Background 5+ years of professional experience in machine learning engineering and/or data science. 2. Education: Preferably MS or PhD in computer science, math, artificial intelligence, or similar disciplines; or BSc with equivalent experience. 3. Machine Learning Expertise Strong knowledge of ML fundamentals: model architectures, data analysis, evaluation methods, and strong statistics and probabilities foundations. Proficient in one or more ML frameworks (e.g., PyTorch, TensorFlow, JAX) and advanced techniques for fine-tuning. Expert at building accurate and high performing models, including evaluation, profiling, iterating, and monitoring. 4. MLOps & Cloud Know-How While not the primary focus, you’re comfortable with containerization, orchestration, and basic cloud services. Experience with monitoring, logging, and CI/CD pipelines to keep ML systems healthy and up-to-date. 5. Mindset & Culture First-principles problem solving: you can start from ambiguity and raw problems and work, with minimal supervision, with product and engineering partners to shape them into clear, impactful solutions. Bias to action : you excel at moving quickly and pragmatically to solve complex problems. Attention to detail : you value clean, maintainable solutions that can scale. Ownership : you take pride in your work, your team’s and your company’s problems are your problems, and you thrive in a collaborative environment. Adaptability : you enjoy learning and can pivot quickly as new challenges arise. Continuous learning: Stay up to date with the latest AI research, sharing expertise and fostering technical growth within the team. Nice to have: Hands-on experience with LLMs, Generative AI, or similar advanced ML domains is a strong advantage. Experience with multimodal AI is a strong advantage. Experience leading or mentoring peers on technical projects. Benefits RXNT offers employees access to medical insurance, 401K (US Candidates), short and long term disability, as well as the potential to earn quarterly incentives based on the company's performance.