Senior Machine Learning Scientist, Foundational ML, AI for Biology & Translation (AIBT)

The Position 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 the 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 We seek a highly motivated Senior ML Scientist to join the Foundation Models team (DELTA) within the AIBT (AI for Biology & Translation) department in Genentech Research and Early Development (gRED). Our team drives cutting‑edge AI research that delivers real‑world impact in drug and target discovery, with a focus on large‑scale foundation models in biology. The successful candidate will contribute to the design and development of the next generation of large‑scale foundation models, with the ultimate aim of accelerating target and drug discovery. In this role, the candidate will advance AI research in multimodal generative modeling, representation learning, LLMs, and/or reinforcement learning, with direct applications to genomics, perturbation biology, imaging, and multimodal experimental data, among others. In this role, you will: Design and build foundation models to support target and drug discovery, with a focus on large‑scale representation learning, multimodal generative models, LLMs, AI agents, and reinforcement learning. Work with and integrate diverse data modalities such as molecular structures, biological sequences, omics data, biochemical readouts, and text. Bridge cutting‑edge AI models and applications supporting target discovery, experimental design, and lab‑in‑the‑loop pipelines. Scale frontier AI models to massive datasets working at the intersection of deep learning and engineering challenges, focusing on system design, architectural choices, and scalability, in collaboration with engineering and MLOps teams. Publish in top‑tier ML venues and scientific journals, and present results at internal and external conferences and workshops. Collaborate closely with interdisciplinary and cross‑functional teams across gRED and Roche. Who you are Educational background Ph.D. in Computer Science, Machine Learning, Computational Biology, or a related quantitative field Experience 0 – 2+ years of industry or post‑doc experience Proven track record advancing ML models in research and/or industry settings, particularly in large‑scale representation learning, multimodal generative models, LLMs, AI agents, and reinforcement learning. Demonstrated interest in advancing AI for scientific applications spanning biology, chemistry, and drug discovery. Technical skills Excellent knowledge of the theory and practice of deep learning. Proven experience developing and delivering innovative ML solutions in the areas above. Excellent Python programming skills, fluency with modern agentic coding environments, and extensive experience with ML frameworks such as PyTorch or JAX. Strong grasp of software engineering, data engineering, and MLOps best practices (e.g., version control, high‑performance compute infrastructures, and ML experiment monitoring workflows). Strong publication record and active contribution to research communities, including top‑tier ML venues such as NeurIPS, ICML, ICLR, AAAI, ACL, EMNLP, AISTATS, etc. and/or public portfolio of relevant projects (e.g. hosted on GitHub/GitLab). Excellent communication, collaboration, and problem‑solving skills. Preferred Practical experience bridging innovative ML methods and applications in target/drug discovery. Experience with biological and chemical modalities and tasks such as molecular structures, single‑cell/omics data, perturbation biology, and multimodal biological datasets. Hands‑on experience developing, fine‑tuning, and optimizing LLMs and agentic systems. Relocation benefits are NOT available for this job posting. The expected salary range for this position based on the primary location of San Francisco is $147,800 - 274,400 of hiring range. Actual pay will be determined based on experience, qualifications, geographic location, and other job‑related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below. Benefits

• ComputationCoE
• tech4lifeComputationalScience

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