寻找你的下一个职业机会

About Care Access Care Access is working to make the future of health better for all. With hundreds of research locations, mobile clinics, and clinicians across the globe, we bring world-class research and health services directly to communities that often face barriers to care. We are dedicated to ensuring that every person has the opportunity to understand their health, access the care they need, and contribute to the medical breakthroughs of tomorrow. With programs like Future of Medicine, which makes advanced health screenings and research opportunities accessible to communities worldwide, and Difference Makers, which supports local leaders to expand their community health and wellbeing efforts, we put people at the heart of medical progress. Through partnerships, technology, and perseverance, we are reimagining how clinical research and health services reach the world. Together, we are building a future of health that is better and more accessible for all. To learn more about Care Access, visit . How This Role Makes a Difference By joining our team as a Senior Machine Learning Engineer, you will play a pivotal role in building cutting-edge AI products that directly impact how new therapies reach patients. We’re looking for an experienced ML engineer who is passionate about turning advanced AI research into scalable, real-world solutions. You thrive on solving complex problems, pay close attention to detail, and consistently seek to automate and improve processes. You shine as a collaborator and excel as an individual contributor, with the courage to tackle challenging problems and the humility to learn and adapt. Your initiative and discipline allow you to thrive while working remotely, and your high degree of empathy and communication skills makes you the kind of colleague everyone wants on their team. As an integral member of our fast-growing organization, you will leverage AI to transform clinical research and improve patient care. How You'll Make An Impact Build and deploy AI-driven products that accelerate clinical trials and improve patient outcomes. Your work will deliver scalable machine learning solutions to complex, real-world problems in clinical research. Develop advanced ML models and LLM-powered agents for critical use cases like patient recruitment, enrollment forecasting, and study feasibility. You’ll also help expand our AI knowledge base architecture to support these innovative solutions. Leverage modern cloud tools and MLOps best practices to build robust data pipelines and deploy models at scale. You’ll use technologies like Python (and Clojure), AWS services (Athena, Bedrock, SageMaker, etc.), dbt, Prefect, and CI/CD automation with monitoring to ensure models are reliable and up-to-date. Collaborate across teams of data scientists, product managers, designers, engineers, and domain experts to integrate AI capabilities into our platform (including Care Access products). Ensure these AI solutions seamlessly support and enhance clinical research workflows for end-users. Continuously learn and innovate. Stay up-to-date with the latest developments in ML/AI (LLMs, NLP, probabilistic modeling, etc.) and proactively bring new ideas to the team. You’ll have the freedom to experiment with cutting-edge techniques and turn promising prototypes into production features that drive our mission forward. The Expertise Required Strong programming and data skills: Proficiency in Python and its ML ecosystem (pandas, scikit-learn, TensorFlow/PyTorch), with clean and efficient coding practices. Comfortable working with large datasets, writing complex SQL queries, and leveraging modern data processing frameworks. Experience with functional programming (e.g. Clojure) is a plus but not required. Cloud and MLOps expertise: Experience with modern cloud infrastructure (AWS or similar) and containerization tools like Docker. Familiarity with MLOps best practices such as CI/CD pipelines, automated testing, and monitoring model performance/data drift to ensure reliable, scalable deployments. Deep ML/AI knowledge: Strong understanding of machine learning fundamentals (model selection, training, evaluation, feature engineering) and statistical modeling. Familiarity with NLP and large language models is important. Analytical problem-solving: Ability to break down complex problems and devise effective, efficient ML solutions. You balance pragmatic engineering with scientific rigor, ensuring models are not only accurate but also performant and maintainable in production. Mission-driven and business-focused mindset: A passion for our mission to speed up clinical trials and improve patient outcomes. Empathy for patients, clinicians, and researchers drives you to build unbiased AI solutions.  Certifications/Licenses, Education, and Experience:   Minimum Experience:Minimum of 5+ years of hands-on experience building and deploying machine learning solutions in production at scale. Proven ability to implement end-to-end ML pipelines from data ingestion to model serving for real-world applications used by real people. How We Work Together Location: Remote within the United States. This role requires 100% of work to be performed in a remote office environment. Travel: This is a remote position with less than 10% travel requirements. Occasional planned travel may be required as part of the role. Physical demands associated with this position Include: The ability to use keyboards and other computer equipment. The expected pay range for this role is $140,000 - $190,000 USD per year for full time team members. Benefits & Perks (US Full Time Employees) Paid Time Off (PTO) and Company Paid Holidays 100% Employer paid medical, dental, and vision insurance plan options Health Savings Account and Flexible Spending Accounts Bi-weekly HSA employer contribution Company paid Short-Term Disability and Long-Term Disability 401(k) Retirement Plan, with Company Match Mandatory Employer Disclosures: Notice to Illinois applicants: Applicants are not obligated to disclose expunged juvenile records or adjudication, arrest, or conviction. Notice to Connecticut applicants: Care Access may require applicants to submit to a urinalysis drug test in connection with an application for employment. Notice to Arizona, Georgia, Indiana, and North Dakota applicants: Care Access complies with applicable laws prohibiting smoking in and around places of employment. Notice to Massachusetts applicants: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Notice to Rhode Island applicants: Care Access complies with Rhode Island law prohibiting smoking in enclosed areas within places of employment. Care Access is also subject to is subject to Chapters 29–38 of Title 28 of the Rhode Island General Laws. Notice to Maryland applicants: UNDER MARYLAND LAW, AN EMPLOYER MAY NOT REQUIRE OR DEMAND, AS A CONDITION OF EMPLOYMENT, PROSPECTIVE EMPLOYMENT, OR CONTINUED EMPLOYMENT, THAT AN INDIVIDUAL SUBMIT TO OR TAKE A LIE DETECTOR OR SIMILAR TEST. AN EMPLOYER WHO VIOLATES THIS LAW IS GUILTY OF A MISDEMEANOR AND SUBJECT TO A FINE NOT EXCEEDING $100. Diversity & Inclusion We work with and serve people from diverse cultures and communities around the world. We are stronger and better when we build a team representing the communities we support. We maintain an inclusive culture where people from a broad range of backgrounds feel valued and respected as they contribute to our mission. We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to, and will not be discriminated against on the basis of, race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, protected veteran status, or any other characteristic protected by law. Care Access is unable to sponsor work visas at this time. If you need an accommodation to apply for a role with Care Access, please reach out to: Originally posted on Himalayas