Senior Machine Learning Engineer - AI - Lab Automation Software

Genentech • Daly City, CA

2d

About 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 this 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.
Within the CoE organisation, the Data and Digital Catalyst (DDC) organisation drives the modernisation of our computational and data ecosystems and integration of digital technologies across Research and Early Development to enable our stakeholders, power data-driven science and accelerate decision-making. As a Senior Machine Learning Engineer for the AI team within the Engineering - Lab Automation capability, you will be a key technical driver, responsible for designing and implementing robust MLOps, system abstractions, and deployment architecture to integrate AI models directly into lab devices. You will solve the most complex technical challenges for the team at the intersection of embedded AI, low-latency inference, and data flow, ensuring that the resulting intelligence layer is scalable and reliable. Your work will be vital in shaping our closed-loop experimentation strategy and enabling autonomous decision making in labs to accelerate drug discovery.

Requirements

• MS/BS in CS or related field with 5+ years of experience, or PhD with 2+ years of experience focused on high-performance ML systems.
• Hands-on experience designing and operating scalable, low-latency production systems for ML models (MLOps, microservices, cloud/edge deployment).
• Expertise in ML performance optimization, including GPU/accelerator utilization and memory management.
• Proven application of engineering best practices including rigorous code review, CI/CD, unit/integration testing for hardware-in-the-loop.
• Expert proficiency in Python; experience with Kubernetes and containerization.
• Proven expertise in data modeling, databases (e.g., relational, NoSQL), and implementing robust MLOps/Cloud infrastructure.
• Experience or a strong interest in interacting with physical systems, including hardware-software integration, robotic middleware (e.g., ROS, drivers/SDKs), or IoT device communication.

Nice To Haves

• Prior experience with distributed systems and control paradigms (e.g., ROS, high-level messaging protocols)
• Practical experience building User Interfaces (UIs) or dashboards to visualize real-time model metrics and system health.

Responsibilities

• Design, develop, and test the foundational software architecture required for high-level experimental orchestration, enabling the transformation of scientific intent into validated, machine-executable programs across diverse robotic lab hardware.
• Design and implement robust, scalable services and APIs to deploy trained ML models, ensuring minimal latency for real-time inference and autonomous control within the physical lab environment.
• Partner with scientists and engineering teams to define data requirements, ensuring the collection of high-quality, multimodal data and metadata necessary for model training and ML readiness.
• Partner with ML scientists, product managers, and scientific domain experts to understand user needs, shape technical requirements, and make optimal choices for model integration and control architecture.
• Constantly improve the performance of the team's MLOps infrastructure and pipelines, acting as the technical liaison with central infrastructure teams for scaling, support, and resource provisioning.
• Contribute to architectural decisions, code reviews, and the evolution of development processes to ensure system scalability and maintainability across the global automation landscape.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees