Job Title: Senior/Principal/ Staff Machine Learning Engineer (Lead role potential)

Location: San Jose, CA (Hybrid onsite/Remote)

Keywords: LLM, NLP, GPU, fine tuning, productionization, MLOps.

Overview:

Leading the healthcare transformation with advanced AI and technology, this team's mission is clear: harness clinical data to deliver actionable insights for improved patient outcomes. Committed to AI's potential to revolutionize healthcare, they offer solutions that are not only faster but also smarter and more reliable. Their platform integrates advanced machine learning with deep clinical expertise to provide precise, actionable insights from complex data sets, ensuring comprehensive clinical intelligence.

Role Overview:

We seek a highly skilled and motivated Machine Learning Engineer to join our dynamic team. In this role, you will design, develop, and deploy machine learning models, including LLM, that enhance our AI-powered clinical intelligence platform. You will work closely with researchers, engineers, and clinical experts to create innovative solutions that drive our mission forward.

Key Responsibilities:

• Develop and implement scalable and reliable AI and machine learning models and algorithms.
• Enable the research and AI development to accelerate overall product development.
• Design and build Services, Tools, and Platforms to support the growth.
• Collaborate with Eng, AI, and clinical experts to understand the intricacies of healthcare data and refine model performance.
• Design and execute experiments to test the efficacy of new models and algorithms.
• Deploy and maintain machine learning and AI models in a production environment, ensuring scalability and reliability.
• Continuously monitor and optimize the performance of deployed models.
• Stay updated with the latest advancements in machine learning and AI, and incorporate new techniques and tools as appropriate.
• Participate in code reviews and contribute to developing best practices for machine learning at Mendel.

Qualifications:

• Bachelor’s or Master’s in Computer Science, Machine Learning, Data Science, or a related field.
• Minimum of 5 years of experience in building AI/ML training and inference pipelines using MLFlow, Kubeflow in GCP, AWS and/or Azure.
• Proven experience in developing and deploying machine learning models.
• Proficiency in programming languages such as Python, R, or Java.
• Proven experience in ML, AI Algorithms, model tuning and optimization.
• Strong understanding of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-Learn).
• Experience with data preprocessing, feature engineering, and model evaluation techniques.
• Proficiency in cloud infrastructure is a plus.
• Familiarity with healthcare data and clinical expertise is a plus.
• Excellent problem-solving skills and attention to detail.
• Ability to work collaboratively in a fast-paced, multidisciplinary environment.
• Strong communication and interpersonal skills, with the ability to convey complex technical concepts to non-technical stakeholders.

What We Offer:

Competitive salary and benefits package.

Opportunity to work with a talented and passionate team at the cutting edge of AI and healthcare.

A collaborative and inclusive work environment that values creativity and innovation.

Professional development opportunities and support for continuous learning.

The chance to make a meaningful impact on the healthcare industry and improve patient outcomes.

About the startup:

US-based Series B startup specializes in using AI tools to analyze clinical data from cancer patients, integrating medical history and genetic analysis. Utilizing AI, specifically Computer Vision and natural language processing, the company extracts and synthesizes medical information from electronic health records and clinical literature. This process generates validated, analytics-ready data for research purposes. The startup aims to revolutionize clinical treatments by leveraging real-world data for virtual experimentation. Their approach simulates disease progression and treatment outcomes, aiming to replace trial-and-error methods with data-driven insights. Their technology not only enhances accuracy and scalability in processing unstructured medical data but also integrates previously disparate data points into a cohesive framework, setting a new standard in healthcare data analytics.