Senior Machine Learning Scientist - Lab Automation

Genentech; San Francisco (CA); On-site; USD 168,000 - 312,000; Full time

Job summary

Genentech, Inc. in San Francisco is seeking a Senior Multimodal Machine Learning Scientist to lead hands‑on development of autonomous lab systems, unifying vision, sensor data, and laboratory telemetry to enable unattended experiments.

You will build end‑to‑end ML pipelines, evaluate models, deploy scalable inference, and collaborate with scientists, automation engineers, and software teams to advance AI‑driven drug discovery.

Responsibilities

• Develop and fine‑tune state‑of‑the‑art vision models (VLMs, DinoV2, SAM, custom detection/segmentation) for real‑time visual QC in lab workcells.
• Integrate camera feeds with time‑series sensor telemetry, system logs, assay metadata, and other lab data to detect physical failure modes.
• Build predictive AI models on spectroscopy, plate‑readers, chromatography data to quantify assay quality and guide closed‑loop experiments.
• Create end‑to‑end ML pipelines: data collection, labeling, training, deployment, monitoring, and iterative improvements.
• Develop agentic pipelines that interpret lab data, reason through workflows, and execute autonomous recovery decisions.
• Construct rigorous eval suites to benchmark model and agent accuracy, robustness, and safety in production labs.
• Deploy and optimize low‑latency ML inference and monitoring infrastructure supporting diverse models and instruments.

Job description

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 organizations 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 maximizing 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

At Genentech (gRED) and Roche (pRED), we’re applying software, robotics, and advanced AI to solve one of humanity’s hardest challenges: building fully autonomous, self-healing laboratories for lights-out drug discovery. Advances in AI, data, and computational sciences are transforming drug discovery and development. The Computational Sciences Center of Excellence (CoE) and the Data and Digital Catalyst (DDC) team are building the foundational platform for autonomous labs—turning frontier AI models, rich physical telemetry, and multi-vendor robotics into resilient, intelligent systems that run complex science unattended around the clock to deliver life-saving medicines to patients faster than ever before.

As a Senior Multimodal Machine Learning Scientist, you will be the hands‑on technical lead and architect developing intelligent systems for autonomous laboratories. Your work will span computer vision and multimodal perception, predictive modeling, agentic systems, and closed-loop experimentation, for example, unifying camera feeds with rich physical telemetry to give lab systems situational awareness and autonomous recovery capabilities. You will build and deploy models and end‑to‑end ML pipelines, from data collection and model development to evaluation and deployment, helping pioneer the next generation of intelligent laboratory infrastructure at global scale.

In this role, you will:

• Develop and fine‑tune state‑of‑the‑art vision models (VLMs, DinoV2, SAM, custom detection/segmentation) to perform real‑time visual quality control across lab workcells (e.g., liquid level sensing, tip verification, labware orientation, spill/foam detection).
• Develop multimodal models that integrate camera feeds and video streams with time‑series sensor telemetry, system logs, assay metadata, and other laboratory data to catch and understand physical failure modes.
• Build predictive AI models on rich analytical data (spectroscopy, plate‑readers, chromatography) to quantify assay quality and support closed‑loop experimental decision‑making.
• Build the data and ML pipelines needed to support these systems end‑to‑end, including image/video and sensor data collection, curation, processing, labeling, model training, deployment, monitoring, and iterative improvement.
• Build agentic pipelines that interpret lab data, reason through multi‑step experimental workflows, and execute autonomous recovery decisions.
• Build rigorous evaluation suites (evals) to systematically benchmark model and agent accuracy, robustness, and decision safety in drug discovery labs.
• Deploy and optimize scalable ML inference and monitoring infrastructure that is low‑latency and can support diverse models, instruments, and scientific use cases.

Who you are:

• MS/PhD in Computer Science, Data Science, AI, or a related quantitative field with 3+ years of hands‑on experience deploying production ML systems or translational research models.
• Passion for translating frontier AI into biological workflows and real‑world therapeutic breakthroughs.
• Strong background in Computer Vision and Vision‑Language Models (VLMs), with hands‑on experience training, fine‑tuning, or evaluating visual perception architectures (e.g., PyTorch, OpenCV, torchvision, object detection/segmentation, 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/image streams with unstructured physical data (sensor logs, time‑series, tabular scientific data).
• Hands‑on experience building, orchestrating, or tuning agent harnesses.
• Demonstrated mastery in building comprehensive evaluation pipelines (evals) to benchmark model and agent accuracy, robustness, and safety in production environments.
• Demonstrated ability to work across disciplines and translate scientific requirements into practical ML systems, collaborating with scientists, automation/robotics engineers, and software engineers.

Preferred, but not required

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

Relocation benefits are not available for this opportunity

The expected salary range for this position, based on the primary location of San Francisco, is $168,100 - 312,300. 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

Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

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