Machine Learning Engineer, Foundation Models (Prescient / AI for Drug Discovery)
Genentech · New York, NY
- On site
- Full-time
- $262,000 / year
- New York, NY
Job highlights
- Develop cutting-edge AI/ML applications for drug discovery.
- Work on the full LLM lifecycle: data to deployment.
- Solve complex backend ML and engineering challenges.
- Collaborate with cross-functional teams in life sciences.
- Requires BS/MS/PhD and 2+ years ML experience.
About the role
Machine Learning Engineer, Foundation Models
A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.
Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness this transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.
The Opportunity
At Roche's AI for Drug Discovery (AIDD) group (Prescient Design), we are revolutionizing drug discovery with cutting-edge machine learning techniques. We are seeking talented engineers with a passion for building large-scale, distributed machine learning algorithms and systems that will transform the drug discovery process. AIDD’s Foundation Model team is seeking an exceptional Machine Learning Engineer to develop the backend of our internal agents platform and enable the next generation of AI/ML-powered drug discovery applications as part of our broader Lab-in-the-Loop approach. We are looking for someone who is not only passionate about technical problem-solving but also has a proven track record of delivering innovative solutions in AI/ML.
The group provides a dynamic and challenging environment for multidisciplinary research, including access to heterogeneous data sources, close links to top academic institutions around the world, as well as collaborations with internal Genentech and Roche teams.
In this role, you will:
- Develop various, cutting-edge internal AI/ML-based applications for drug discovery and development.
- Develop across the lifecycle of LLMs including data engineering, pre-training, fine-tuning, post-training, and evaluation.
- Deploy models in production environments, working closely with other engineers to ensure scalability and reliability.
- Solve core backend challenges in ML and engineering including the design, implementation, and scaling of our data, training, and deployment pipeline.
- Collaborate closely with cross-functional teams across both AIDD and Roche to solve complex problems in the life sciences.
Who You Are
You have a BS/MS in Computer Science, Statistics, related field, or equivalent experience and 2+ years of industry experience in machine learning; or a PhD in Computer Science and related field and 0+ years of industry experience. You have demonstrated success in technical capabilities deploying machine learning models in industry-grade production environments. Experience in back-end development for LLM-powered agents and tools, or working with Model Context Protocol (MCP). You have experience in developing LLM/GenAI products and are familiar with open-source AI frameworks like Open WebUI. Strong interest in solving problems in drug discovery and biomedical science. You have strong programming skills in Python and using deep learning frameworks like PyTorch. You have extensive experience with deep learning & LLMs. A collaborative and open-minded team player who values constructive feedback, refrains from judgment, and fosters a positive and respectful team environment. Strong communication skills, with the ability to effectively communicate technical concepts to both technical and non-technical audiences as well as interfacing with scientific and engineering leadership. A passion for solving complex technical problems and a commitment to staying up-to-date with the latest developments in machine learning. An extensive track record of delivering innovative solutions in machine learning.
Key skills/competency
- Machine Learning
- Foundation Models
- Large Language Models (LLMs)
- AI/ML Applications
- Drug Discovery
- Backend Development
- Python
- PyTorch
- Deep Learning
- Production Deployment
Skills & topics
- Machine Learning Engineer
- AI
- Foundation Models
- LLM
- Drug Discovery
- Backend Development
- Python
- PyTorch
- Deep Learning
- Genentech
- Roche
- Computational Sciences
- Production Deployment
How to get hired
- Tailor your resume: Highlight your experience with machine learning, LLMs, Python, and PyTorch, specifically mentioning production deployments and backend development for AI/ML applications.
- Showcase your passion: Emphasize your strong interest in drug discovery and biomedical science, and your track record of delivering innovative ML solutions.
- Demonstrate collaboration: Prepare to discuss your ability to work effectively in cross-functional teams and communicate complex technical concepts to diverse audiences.
- Research Genentech: Understand the company's mission in advancing science and healthcare, and their use of AI in drug discovery to align your application with their goals.
- Prepare for technical interviews: Be ready to discuss your experience with deep learning frameworks like PyTorch and backend development for LLM agents.
Technical preparation
Behavioral questions
Frequently asked questions
- What specific AI/ML applications will a Machine Learning Engineer develop at Genentech's AIDD group?
- As a Machine Learning Engineer at Genentech's AI for Drug Discovery (AIDD) group, you will develop internal AI/ML-based applications aimed at revolutionizing the drug discovery and development process. This includes working with foundation models and LLMs to create next-generation AI/ML-powered applications within their Lab-in-the-Loop framework.
- What is the role of Foundation Models in this Machine Learning Engineer position?
- Foundation Models are central to this role. You will be developing the backend for internal agents platforms, focusing on the entire lifecycle of LLMs, including data engineering, pre-training, fine-tuning, post-training, and evaluation, to enable advanced AI/ML applications in drug discovery.
- What programming languages and frameworks are essential for this Machine Learning Engineer role?
- Strong programming skills in Python are essential for this Machine Learning Engineer position. You should also have experience with deep learning frameworks such as PyTorch, and familiarity with open-source AI frameworks like Open WebUI.
- What kind of experience is required for the Machine Learning Engineer position at Genentech?
- The role requires a BS/MS in Computer Science or a related field with at least 2 years of industry experience in machine learning, or a PhD with 0+ years of experience. Demonstrated success in deploying ML models in production and experience with LLM backend development are key.
- How does Genentech use AI in drug discovery?
- Genentech leverages advances in AI, data, and computational sciences to transform drug discovery and development. They use novel computational models and data to accelerate R&D and are building a new Computational Sciences Center of Excellence (CoE) to harness AI's power for their scientists.
- Is relocation assistance provided for this Machine Learning Engineer job?
- No, relocation benefits are not available for this Machine Learning Engineer job posting at Genentech.
- What are the primary responsibilities for a Machine Learning Engineer in the AIDD Foundation Model team?
- Primary responsibilities include developing AI/ML applications, managing the LLM lifecycle, deploying models in production, solving backend ML/engineering challenges related to data, training, and deployment pipelines, and collaborating with cross-functional teams.
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