Senior Machine Learning Scientist - Lab Automation

Roche • South San Francisco, CA

1d • $168,100 - $312,300

About The Position

Genentech and Roche’s Computational Sciences Center of Excellence and Data and Digital Catalyst team are building foundational platforms for autonomous laboratories, combining frontier AI models, physical telemetry, and multi-vendor robotics to support unattended drug discovery. The Senior Machine Learning Scientist is a hands-on technical lead and architect developing intelligent systems for autonomous laboratories. The role spans computer vision and multimodal perception, predictive modeling, agentic systems, and closed-loop experimentation, including unifying camera feeds with physical telemetry to provide laboratory systems with situational awareness and autonomous recovery capabilities. The position develops and deploys models and end-to-end machine learning pipelines, from data collection and model development through evaluation and deployment, to help advance intelligent laboratory infrastructure at global scale.

Requirements

• MS or PhD in Computer Science, Data Science, AI, or a related quantitative field.
• At least 3 years of hands-on experience deploying production ML systems or translational research models.
• Strong background in computer vision and Vision-Language Models, with hands-on experience training, fine-tuning, or evaluating visual perception architectures.
• Hands-on experience with tools or methods such as PyTorch, OpenCV, torchvision, object detection and segmentation, or vision-text transformers.
• Strong foundations in machine learning, statistics, and data analysis, with hands-on experience developing and deploying predictive and data-driven models.
• Proven track record building and deploying multimodal models that combine camera feeds or image streams with unstructured physical data, sensor logs, time-series data, or tabular scientific data.
• Hands-on experience building, orchestrating, or tuning agent harnesses.
• Demonstrated mastery in building comprehensive evaluation pipelines to benchmark model and agent accuracy, robustness, and safety in production environments.
• Ability to work across disciplines and translate scientific requirements into practical ML systems, collaborating with scientists, automation and robotics engineers, and software engineers.
• Passion for translating frontier AI into biological workflows and real-world therapeutic breakthroughs.

Nice To Haves

• Experience with synthetic vision data generation, video-based anomaly detection, or edge-vision deployment.
• Familiarity with laboratory automation hardware, analytical instruments such as liquid handlers, plate readers, and HPLC, or industrial IoT telemetry.
• Exposure to Bayesian optimization, active learning, reinforcement learning, or Design of Experiments for closed-loop experimentation.
• Public GitHub or GitLab portfolio showcasing production ML pipelines, computer vision projects, agentic frameworks or evaluations, or multimodal models.

Responsibilities

• Develop and fine-tune vision models, including VLMs, DinoV2, SAM, and custom detection and segmentation models, for real-time visual quality control across laboratory workcells.
• Develop multimodal models integrating camera feeds and video streams with time-series sensor telemetry, system logs, assay metadata, and other laboratory data to detect and understand physical failure modes.
• Build predictive AI models using analytical data such as spectroscopy, plate-reader, and chromatography data to quantify assay quality and support closed-loop experimental decision-making.
• Build end-to-end data and ML pipelines for image, video, and sensor data collection, curation, processing, labeling, model training, deployment, monitoring, and iterative improvement.
• Build agentic pipelines that interpret laboratory data, reason through multi-step experimental workflows, and execute autonomous recovery decisions.
• Build evaluation suites to benchmark model and agent accuracy, robustness, and decision safety in drug discovery laboratories.
• Deploy and optimize scalable, low-latency ML inference and monitoring infrastructure supporting diverse models, instruments, and scientific use cases.

Benefits

• A discretionary annual bonus may be available based on individual and Company performance.
• The position qualifies for company benefits detailed at the linked benefits page.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior