Remote E Learning Jobs in Massachusetts (NOW HIRING)

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.

• 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.

• 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.
• 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.

The expected pay range for this role is $140,000 - $190,000 USD per year for full time team members.

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