Senior AI Engineer- Imaging

Genentech; San Francisco (CA); Presencial; USD 168.000 - 312.000; Jornada completa

Descripción de la vacante

Genentech is hiring an imaging-focused AI/ML Engineer to build scalable computer vision systems for large-scale scientific imaging data. You will design robust pipelines, train models for cell segmentation, tissue segmentation, and image registration, and collaborate with scientists to translate imaging needs into technical specs.

The role involves developing cloud infrastructure, optimizing GPU usage, and contributing to MLOps practices while staying current with emerging CV technologies.

Formación

• PhD/Master/Bachelor with focus on ML engineering and computer vision
• Strong proficiency with AI/ML frameworks and CV libraries (PyTorch, TensorFlow, OpenCV, MONAI, Scikit-Image, Napari, Bio-Formats)
• Deep expertise in CNNs, Vision Transformers, UNet, SAM, Cellpose
• Expertise in ML theory and image processing (object detection, instance segmentation)
• Experience with high-dimensional scientific imaging data (e.g., fluorescence, digital pathology)
• Knowledge of GPU acceleration and optimization (CUDA)
• Experience with Kubernetes, relational/NoSQL databases, object storage, cloud-native architectures (AWS)
• Proven software engineering practices (CI/CD, MLOps)
• Excellent communication and cross-functional collaboration
• Able to rapidly learn new technologies and domains

Responsabilidades

• Design, develop, and test scalable AI/ML CV web apps and backend pipelines for high-throughput imaging
• Build and fine-tune CV/DL models for cellular and tissue imaging use cases
• Create tools to evaluate model performance and quantify drift over time
• Collaborate with product managers and scientists to translate imaging needs into specs
• Develop and maintain scalable cloud infrastructure and data pipelines for large-scale imaging datasets (OME-TIFF, Zarr, HDF5)
• Optimize GPU memory usage, batching, and inference throughput
• Participate in architectural decisions, code reviews, and MLOps practices
• Span the full stack and contribute beyond core expertise
• Stay current on CV tech and foster learning and collaboration

Conocimientos

AI/ML

PyTorch

TensorFlow

OpenCV

MONAI

Scikit-Image

Napari

Bio-Formats

CNNs

Vision Transformers

UNet

SAM

Cellpose

CUDA

Kubernetes

AWS

CI/CD

MLOps

Communication

Educación

PhD, Master’s (3–5 years), or Bachelor’s (4–7 years) in a related technical field

Herramientas

PyTorch

TensorFlow

OpenCV

MONAI

Scikit-Image

torchvision

Napari

Bio-Formats

CUDA

Kubernetes

AWS

Descripción del empleo

Genentech is hiring an imaging-focused AI/ML Engineer to build and continuously improve scalable computer vision systems for large-scale scientific imaging data.

Responsibilities

• Design, develop, and test robust, scalable, and maintainable AI/ML scientific web applications and backend pipelines for high-throughput imaging processing and visualization.
• Build, evaluate, and fine-tune state-of-the-art computer vision and deep learning models for scientific imaging use cases such as cell segmentation, cell classification, phenotypic profiling, tissue segmentation, feature extraction, and image registration.
• Create tools to evaluate AI/ML and vision model performance, define 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 scientific imaging needs into technical specifications.
• Develop and maintain scalable cloud infrastructure and data pipelines to collect, structure, store, and enable access to large-scale multi-dimensional imaging datasets including OME-TIFF, Zarr, and HDF5.
• Optimize GPU memory usage, batching, and inference throughput for large image pipelines and deep neural networks.
• Participate in architectural decisions, code reviews, and the evolution of engineering and MLOps practices for computer vision systems.
• Span the full stack and contribute beyond core expertise when needed.
• Stay current on emerging computer vision technologies and best practices, and support a culture of continuous learning and collaboration.

Requirements

• PhD, Master’s (3–5 years), or Bachelor’s (4–7 years) in a related technical field with focus on machine learning engineering and computer vision (Computer Science, Biomedical Engineering, Electrical Engineering, Computational Biology, or similar).
• Strong proficiency with AI/ML frameworks and computer vision libraries, including PyTorch, TensorFlow, OpenCV, MONAI, Scikit-Image, torchvision, Napari, and/or Bio-Formats.
• Deep expertise in computer vision algorithms such as CNNs, Vision Transformers, UNet, SAM (Segment Anything), and Cellpose.
• Expertise in machine learning theory and image processing fundamentals including object detection, instance segmentation, and quantitative image analysis.
• Demonstrated experience with high-dimensional scientific imaging data (for example: fluorescence/brightfield microscopy, digital pathology whole slide images, high-content screening, multiplexed imaging).
• Strong knowledge of GPU acceleration and parallel computing, including 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 software engineering best practices, including CI/CD and MLOps principles.
• Excellent communication skills and ability to build trusted partnerships with scientific collaborators and cross-functional teams.
• Ability to rapidly learn new technologies, domain knowledge, and programming languages with a focus on continuous learning.

Technologies

• PyTorch, TensorFlow, OpenCV, MONAI, Scikit-Image, torchvision, Napari, Bio-Formats
• CNNs, Vision Transformers, UNet, SAM/Segment Anything, Cellpose
• CUDA, Kubernetes, AWS
• OME-TIFF, Zarr, HDF5

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

• Onsite presence on the South San Francisco campus expected at least 3 days per week.

Relocation Assistance

• Relocation assistance is not available for this posting.

Compensation

• Expected salary range: $168,100 - $312,300 annually (San Francisco, CA primary location).
• Open to SE6 and SE7 levels; final leveling determined after the interview process.
• A discretionary annual bonus may be available based on individual and Company performance.
• Actual pay determined based on experience, qualifications, geographic location, and other factors permitted by law.