Senior AI Engineer- Imaging

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

Benefits offered by this job

Onsite presence at South San Francisco

Discretionary annual bonus

Job summary

Genentech is seeking an imaging-focused machine learning engineer to design, build, and optimize scalable AI/ML systems for high‑throughput scientific imaging data. You will work with computational scientists, biologists, and data scientists to enable advanced phenotypic analysis and multimodal capabilities across large datasets.

You will contribute to model development (CNNs, ViTs, UNet), GPU optimization, and cloud‑based pipelines, while collaborating with cross‑functional teams and upholding

Qualifications

• PhD, Master’s with 3–5 years, or Bachelor’s with 4–7 years in a related field with ML/vision focus.
• Strong proficiency with AI/ML frameworks and computer vision libraries (e.g., PyTorch, TensorFlow, OpenCV, MONAI, Scikit‑Image, torchvision, Napari, Bio‑Formats).
• Deep expertise in CNNs, Vision Transformers, UNet, SAM/Segment Anything, Cellpose.
• Experience with high‑dimensional scientific imaging data (fluorescence/brightfield microscopy, digital pathology, etc.).
• Knowledge of GPU acceleration, CUDA, and ML performance optimization for large image datasets.
• Experience with Kubernetes, relational and NoSQL databases, and cloud-native architectures (AWS).
• Software engineering best practices, CI/CD, and MLOps.

Responsibilities

• Design, develop, and test scalable AI/ML scientific web apps and pipelines for high-throughput imaging.
• Build and tune state-of-the-art computer vision models for biological/clinical imaging.
• Develop benchmarks to measure model quality, drift, and robustness over time.
• Collaborate with scientists to translate imaging needs into technical specs.
• Develop scalable cloud data pipelines and storage for large multi‑dim imaging datasets.
• Optimize GPU memory, batching, and inference throughput for large pipelines.
• Contribute to architectural decisions and CI/CD/MLOps processes.
• Engage across full stack when needed and stay current with CV tech.

Skills

PyTorch

TensorFlow

OpenCV

MONAI

Napari

CUDA

GPU optimization

ML pipelines

Kubernetes

AWS

Education

PhD or MS/BS in CS/BME/EE/Computational Biology or related field

Tools

Kubernetes

Relational databases

NoSQL databases

Object storage

Cloud-native architectures (AWS)

Job description

The Position

We advance science so that we all have more time with the people we love.

As an imaging-focused machine learning engineer within the AI Enablement team, you will be working closely with folks that span the gamut across computational scientists, biologists, bench scientists, AI/ML experts, product leaders, software engineers, and everyone in between. Your work will help bridge scientific intuition with quantitative analysis, enable richer phenotypic analysis, and provide new multimodal capabilities across large-scale, state-of-the‑art datasets. You'll build, own, and constantly improve scalable AI/ML and computer vision systems that unlock the potential of our diverse scientific imaging data (e.g., microscopy, digital pathology, biomedical imaging, etc), accelerating the discovery and development of life‑changing treatments for patients.

The Opportunity:

• Design, develop, and test robust, scalable, and maintainable AI/ML scientific web applications and backend pipelines tailored for high-throughput scientific imaging data processing and visualization.
• Build, evaluate, and fine-tune state-of-the‑art computer vision and deep learning models (e.g., cell segmentation, cell classification, phenotypic profiling, tissue segmentation, feature extraction, and image registration) applied to biological and clinical imaging modalities.
• Build tools to evaluate AI/ML and vision model performance, establish benchmark datasets, and quantify model quality, robustness, and drift over time.
• Partner with product managers, bioimage analysts, and research scientists to understand user workflows, shape requirements, and translate complex scientific imaging needs into actionable technical specifications.
• Develop and maintain scalable cloud infrastructures and data pipelines for collecting, structuring, storing, and accessing large-scale multi‑dimensional imaging datasets (e.g., OME‑TIFF, Zarr, HDF5).
• Optimize GPU memory footprint, batching, and inference throughput for large image pipelines and deep neural network models.
• Contribute to architectural decisions, code reviews, and the evolution of engineering and MLOps processes for computer vision models.
• Be willing to span the full stack and contribute where needed, even outside of core areas of expertise.
• Stay up‑to‑date with emerging computer vision technologies and industry best practices while promoting a culture of continuous learning, collaboration, and curiosity.

Who You Are

• PhD, Master’s with 3–5 years, or Bachelor’s with 4–7 years experience with a degree in Computer Science, Biomedical Engineering, Electrical Engineering, Computational Biology, or a related technical field with focus on machine learning engineering and computer vision.
• Strong proficiency with AI/ML frameworks and computer vision libraries (e.g., PyTorch, TensorFlow, OpenCV, MONAI, Scikit‑Image, torchvision, Napari, or Bio‑Formats).
• Deep expertise in computer vision algorithms (e.g., CNNs, Vision Transformers, UNet, SAM/Segment Anything, Cellpose).
• Expert knowledge of machine learning theory, image processing fundamentals, object detection, instance segmentation, and quantitative image analysis.
• Demonstrated experience working with high‑dimensional scientific imaging data (e.g., fluorescence/brightfield microscopy, digital pathology whole slide images, high‑content screening, multiplexed imaging, etc).
• Strong knowledge of GPU acceleration, parallel computing, CUDA, and ML performance optimization for large image datasets.
• Experience with Kubernetes, relational databases, NoSQL databases, object storage, and cloud-native architectures in public clouds (ideally AWS).
• Proven understanding and application of software engineering best practices, CI/CD, and MLOps principles.
• Excellent communication skills and ability to build trusted partnerships with scientific collaborators, bioimage analysts, and cross‑functional teams.
• Ability to quickly acquire new technologies, domain knowledge, and programming languages with a passion for continuous learning.

Preferred Qualifications

• Experience with multi‑modal data fusion (combining scientific imaging with genomics, transcriptomics, or clinical tabular data).
• Experience working directly with biomedical scientists or in a pharma/biotech research environment.
• Experience with workflow automation, GenAI, Vision‑Language Models, and/or AI agents.

Onsite presence on our South San Francisco campus is expected for at least 3 days a week.

Relocation Assistance benefits are not available for this posting.

The expected salary range for this position based on the primary location of San Franscico, CA is $168,100- $312,300 Annual. This position is open to both SE6 and SE7 levels, with final leveling to be determined upon completion of the interview process. 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 here:

Benefits

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.

If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.