Senior Machine Learning Engineer

About the Role

The company's mission to advance clinical research and improve patient care is ambitious, and your role will play a pivotal part in achieving this goal. You will thrive in a remote environment, leveraging modern cloud tools and MLOps best practices to build robust data pipelines and deploy models at scale. Your work will involve collaborating with diverse stakeholders to ensure AI solutions seamlessly support and enhance clinical research workflows for end-users.

OneStudyTeam values innovation and continuous learning, providing the freedom to experiment with cutting-edge techniques and turn promising prototypes into production features. The company's commitment to staying at the forefront of ML/AI technologies, including LLMs, NLP, and probabilistic modeling, means you will have the opportunity to stay updated with the latest developments and apply them to real-world challenges.

What You Will Do

• Build and deploy AI-driven products that accelerate clinical trials and improve patient outcomes.
• Develop advanced ML models and LLM-powered agents for critical use cases like patient recruitment, enrollment forecasting, and study feasibility.
• Expand the AI knowledge base architecture to support innovative solutions.
• Leverage modern cloud tools and MLOps best practices to build robust data pipelines and deploy models at scale.
• Collaborate with data scientists, product managers, designers, engineers, and domain experts to integrate AI capabilities into the platform.
• Ensure AI solutions seamlessly support and enhance clinical research workflows for end-users.
• Stay updated with the latest developments in ML/AI and proactively bring new ideas to the team.
• Experiment with cutting-edge techniques and turn promising prototypes into production features.
• Use technologies like Python, AWS services, dbt, Prefect, and CI/CD automation with monitoring to ensure models are reliable and up-to-date.

What We Are Looking For

• Extensive ML engineering 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.
• Strong programming and data skills: Proficiency in Python and its ML ecosystem.
• Experience with large datasets, writing complex SQL queries, and leveraging modern data processing frameworks.
• Cloud and MLOps expertise: Experience with modern cloud infrastructure and containerization tools like Docker.
• Familiarity with MLOps best practices such as CI/CD pipelines, automated testing, and monitoring model performance/data drift.
• Deep ML/AI knowledge: Strong understanding of machine learning principles and practices.
• Excellent collaboration and communication skills, with the ability to work effectively in a remote environment.

Nice to Have

• Experience with functional programming (e.g., Clojure).
• Familiarity with AWS services (Athena, Bedrock, SageMaker, etc.).
• Knowledge of dbt, Prefect, and CI/CD automation with monitoring.
• Experience working in the healthcare or clinical research sector.

Benefits and Perks

• The opportunity to work on cutting-edge AI products that directly impact patient care and outcomes.
• Collaborative and dynamic work environment with a team of experienced professionals.
• Flexible remote work arrangements, allowing you to work from anywhere.
• Access to the latest technologies and tools in machine learning and cloud computing.
• Professional development opportunities, including training and education in ML/AI and related fields.
• Competitive compensation package, reflecting the importance of this role in the company's mission.
• Health and wellness programs, supporting your physical and mental well-being.
• Generous PTO policy, allowing for a healthy work-life balance.

• Ensure your portfolio highlights your experience with machine learning solutions, especially those related to the healthcare sector.
• Develop a strong understanding of cloud infrastructure and MLOps best practices to stand out in the application process.
• Practice explaining complex technical concepts in simple terms, as this will be crucial in collaborating with non-technical stakeholders.
• Be prepared to discuss your approach to staying updated with the latest developments in ML/AI and how you apply them to real-world problems.
• Consider obtaining certifications in ML/AI or related technologies to demonstrate your commitment to professional development and expertise.
• When negotiating salary, emphasize your relevant experience, skills, and the value you can bring to the company's mission, rather than just relying on industry standards.