Machine Learning Engineer
Paradigm
This job is no longer accepting applications
See open jobs at Paradigm.See open jobs similar to "Machine Learning Engineer" F-Prime Capital Partners.Paradigm is rebuilding the clinical research ecosystem by creating a platform that enables equitable access to trials for all patients while enhancing trial efficiency and reducing the barriers to participation for healthcare providers. Incubated by ARCH Venture Partners and backed by leading healthcare and life sciences investors, Paradigm aims to break down barriers across the trial ecosystem through one seamless infrastructure implemented at healthcare provider organizations, bringing potentially life-saving therapies to patients faster.
Our team is diverse in its experience and 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.
We are looking for a Machine Learning Engineer to join our team. In this role, you will contribute to building NLP and LLM-based models to streamline trial execution activities and integrate them into provider workflows. This is an opportunity to contribute to developing innovative tools to improve protocol design, increase patient recruitment, and speed up clinical trial conduct. Your work will positively impact the lives of patients and help move the science and practice of clinical research forward.
What You'll Do:
- Build, test, and deploy ML models and pipelines at scale, working collaboratively with other engineers and data scientists.
- Collaborate with clinicians, informaticists, and engineers to implement solutions that align with company goals.
- Develop and maintain GenAI/LLM-based models to streamline trial execution activities, including gathering data, training, and evaluating models.
- Work with engineering teams to integrate models into production systems and build tools for monitoring and improving model performance.
- Communicate technical insights and findings to both technical and non-technical stakeholders within the company.
Who You Are:
- Masters or PhD in statistics, computer science, or a related field.
- 2+ years of experience as a machine learning engineer or data scientist, ideally in or adjacent to the healthcare industry.
- Hands-on experience in training, evaluating, deploying, and fine-tuning ML models.
- Proficiency with Python, SQL and familiarity with various machine learning algorithms and statistics.
- Experience in working in production-level engineering architecture, including pipelines and model deployment.
- Strong problem-solving skills and ability to collaborate effectively across teams.
- Comfortable in a fast-paced, mission-driven startup environment.
Bonus:
- Experience working with oncology and/or clinical trials data.
- Familiarity with developing GenAI/LLM-based models and working with open-source frameworks for LLM applications.
- Previous experience in an early-stage startup environment is a plus.
At Paradigm, we are committed to providing equal employment opportunities to all qualified individuals. We believe in creating a diverse and inclusive workplace that values the contributions of every employee, regardless of their race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other characteristic protected by law. We are an equal-opportunity employer and do not discriminate against any employee or applicant for employment based on any of the aforementioned factors. We encourage and welcome candidates from diverse backgrounds and perspectives to apply for our open positions. We strive to provide reasonable accommodations to qualified individuals with disabilities and to ensure that all employment decisions are based on job-related factors such as skills, experience, and qualifications.
This job is no longer accepting applications
See open jobs at Paradigm.See open jobs similar to "Machine Learning Engineer" F-Prime Capital Partners.