Senior Machine Learning Engineer – Foundational ML, AI for Biology, Translation

Roche

Senior ML Engineer developing and scaling foundation models for Roche's AI initiatives in drug discovery. Collaborating with teams to transform biological data into impactful medicines.

Posted 7/24/2026full-timeCalifornia • California • 🇺🇸 United StatesSenior💰 $147,800 - $274,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying scalable ML systems, with a strong focus on MLOps and AgentOps practices. Proficient in Python programming and AWS infrastructure management, with a solid understanding of deep learning frameworks and CI/CD processes.

Highest-signal resume keywords

Python ProgrammingMLOps Lifecycle ManagementAWS Infrastructure DesignCI/CD Pipeline ManagementDeep Learning Frameworks

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Hard Skills

Machine LearningDeep LearningFoundation ModelsModel EvaluationExperiment TrackingDistributed TrainingAutomation ScriptingData EngineeringInfrastructure-as-CodeHigh-Performance Compute

Soft Skills

Problem-SolvingCommunicationCollaborationProject LeadershipAdaptability

Tools & Technologies

AWS EC2AWS S3AWS EKSAWS SageMakerTerraformHelmKubernetesGitCI/CD ToolsML Frameworks

Industry Keywords

AI for ScienceBiologyChemistryDrug DiscoveryQuantitative Field

Tech Stack

Tools & technologies

AWSEC2KubernetesPythonPyTorchTerraform

About the role

Key responsibilities & impact

Build, scale, and productionize foundation models and AI agents that support target discovery, experimental design, and lab-in-the-loop pipelines.
Design and operate the AgentOps and MLOps backbone for these systems, including experiment tracking, model and agent evaluation, monitoring, and reproducible training and inference workflows.
Design, implement, and maintain scalable and reliable ML infrastructure on AWS, and optimize distributed training and inference on high-performance compute.
Manage and optimize CI/CD pipelines and Git repositories for ML projects, ensuring efficient version control to support collaboration and deployment.
Automate deployment, monitoring, and operational tasks using infrastructure-as-code and orchestration tooling (e.g., Terraform, Helm, Kubernetes).
Proactively identify issues and gaps, propose improvements, and champion engineering best practices and code quality across the team.
Collaborate closely with interdisciplinary and cross-functional teams across gRED and Roche, and help support research output where relevant, including publications.

Requirements

What you’ll need

Educational background: BS/MS in Computer Science, Machine Learning, Engineering, or a related quantitative field.
Experience: 5+ years of industry experience building and delivering ML systems.
Technical skills: Excellent Python programming skills, and proficiency in scripting languages for automation.
Solid working knowledge of the theory and practice of deep learning, and hands-on experience with ML frameworks such as PyTorch or JAX.
Practical experience building, finetuning, deploying, and scaling foundation models, LLMs, and/or agentic systems in production.
Hands-on experience with the MLOps and/or AgentOps lifecycle, including experiment tracking, model and agent evaluation, monitoring, and reproducible training and inference workflows.
Proven experience designing, deploying, and managing ML infrastructure on Amazon Web Services (AWS), including services such as EC2, S3, EKS, and SageMaker, along with distributed training and inference on high-performance compute.
Strong software and data engineering fundamentals, with a proven track record of owning production CI/CD pipelines, Git-based workflows, automated testing, and documentation.
Demonstrated ability to lead technical projects from conception to completion and deliver high-quality, scalable, and reliable software.
Excellent problem-solving, communication, and collaboration skills, with the ability to thrive in a fast-paced, user-facing environment.
Preferred: Familiarity with agent orchestration frameworks (e.g., LangGraph, LangChain) and common patterns such as tool use, retrieval, and multi-step workflows.
Interest or experience in applying ML to scientific discovery (AI for science), such as biology, chemistry, or drug discovery, including working with domain-specific data and models.

Benefits

Comp & perks

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.