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Senior Machine Learning Engineer

Remote

Paradigm is rebuilding the clinical research ecosystem by enabling equitable access to trials for all patients. Our platform enhances trial efficiency and reduces the barriers to participation for healthcare providers. Incubated by ARCH Venture Partners and backed by leading healthcare and life sciences investors, Paradigm’s seamless infrastructure implemented at healthcare provider organizations, will bring potentially life-saving therapies to patients faster.

Our team hails from a broad range of disciplines and is committed to the company’s mission to create equitable access to clinical trials for any patient, anywhere. Join us, and bring your expertise, passion, creativity, and drive as we work together to realize this mission.

As a Senior Machine Learning Engineer, you will take a leading role in designing and deploying sophisticated ML models, including GenAI and LLM-based solutions, that optimize clinical trial workflows and patient engagement. This position offers an opportunity to impact healthcare by developing state-of-the-art models that enhance trial design, accelerate patient recruitment, and improve overall trial efficiency. You will contribute to both high-level architecture decisions and hands-on implementation, driving the technology forward and influencing ML strategies across Paradigm.

Responsibilities:

  • Model Development & Deployment: Lead the development, testing, and deployment of ML models and pipelines, with a focus on scalability and integration into production systems.
  • Advanced GenAI/LLM Applications: Design and refine GenAI/LLM-based models to streamline and automate clinical trial operations, from data gathering to real-time performance monitoring.
  • Cross-Functional Collaboration: Partner with clinicians, informaticists, data scientists, and engineers to build solutions aligned with Paradigm’s mission and goals.
  • Infrastructure & Performance Optimization: Drive improvements in model deployment infrastructure, develop monitoring tools, and refine model performance to ensure robust production-level reliability.
  • Technical Leadership & Mentorship: Mentor junior ML engineers, contributing to team knowledge-sharing and establishing best practices for data science and machine learning.
  • Strategic Communication: Present complex technical insights and results to both technical and non-technical stakeholders, advocating for data science-driven strategies that align with business objectives.

Qualifications:

  • Education: Master’s or PhD in computer science, statistics, machine learning, or a related field.
  • Experience: 5+ years of experience as a machine learning engineer, with a proven track record in healthcare, life sciences, or a related field.
  • Technical Skills: Deep expertise in training, fine-tuning, and deploying ML models, including experience with GenAI/LLMs. Proficiency in Python, SQL, and familiarity with cloud infrastructure and ML engineering best practices.
  • Production-Level ML Expertise: Experience managing production-level pipelines, including model deployment, monitoring, and continuous integration.
  • Problem Solving & Collaboration: Advanced analytical skills and a collaborative approach to solving complex challenges across teams.
  • Startup Mindset: Adaptability and experience in fast-paced, mission-driven environments with high levels of ambiguity.

Preferred:

  • Healthcare/Clinical Trials Experience: Background in working with oncology or clinical trial data.
  • GenAI/LLM Proficiency: Hands-on experience developing and deploying GenAI/LLM-based models and open-source frameworks for LLM applications.
  • Startup Experience: Previous involvement in an early-stage startup, ideally in health tech or life sciences, with a passion for high-growth projects.

At Paradigm, we are committed to providing equal employment opportunities to all qualified individuals. We encourage and welcome candidates from all backgrounds and perspectives to apply for our open positions. We are interested in all qualified individuals and  ensure that all employment decisions are based on job-related factors such as skills, experience, and qualifications.

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