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About This Role
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.
The Opportunity
At Roche's AI for Drug Discovery (AIDD) group (Prescient Design), we are revolutionizing drug discovery with cutting-edge machine learning techniques. We are seeking a highly motivated and skilled ML Infrastructure DevOps Engineer to join our growing team within Genentech Research and Early Development AI Drug Development (gRED AIDD). This role is crucial for building and maintaining the scalable and robust infrastructure that powers our machine learning initiatives. The ideal candidate will be proactive, user-facing, and possess a "get-it-done" attitude, while consistently adhering to corporate standards and best practices.
What you'll do
Machine Learning Engineer - DevOps
- Design, implement, and maintain scalable and reliable ML infrastructure on AWS.
- Automate deployment, monitoring, alerting, and operational tasks using tools like Terraform and Helm.
- Manage and optimize CI/CD pipelines and Git repositories for ML projects, ensuring efficient version control to support collaboration and deployment.
- Collaborate closely with ML engineers and data scientists to understand their infrastructure needs and provide solutions.
- Troubleshoot and resolve infrastructure-related issues in a timely manner.
- Implement and enforce security best practices for ML infrastructure.
- Document infrastructure designs, processes, and operational procedures.
- Contribute to initiatives independently as part of a team, delivering assigned outputs.
- Proactively identify issues and gaps, proposing ideas and suggestions for improvements.
Senior Machine Learning Engineer - DevOps
- Lead the architecture and delivery of significant technical solutions
- Mentor junior engineers and drive technical alignment and influence across teams
- Design, implement, and maintain scalable and reliable ML infrastructure on AWS.
- Demonstrated ability to lead technical projects from conception to completion and deliver high-quality, scalable, and reliable software." to "Who you are
- Automate deployment, monitoring, alerting, and operational tasks using tools like Terraform and Helm.
- Manage and optimize CI/CD pipelines and Git repositories for ML projects, ensuring efficient version control to support collaboration and deployment.
- Collaborate closely with ML engineers and data scientists to understand their infrastructure needs and provide solutions.
- Troubleshoot and resolve infrastructure-related issues in a timely manner.
- Implement and enforce security best practices for ML infrastructure.
- Document infrastructure designs, processes, and operational procedures.
- Contribute to initiatives independently as part of a team, delivering assigned outputs.
- Proactively identify issues and gaps, proposing ideas and suggestions for improvements.
Who you are
Machine Learning Engineer - DevOps
- BS/MS with 2-3 years of industry experience required
- Proven experience in designing, deploying, and managing infrastructure on Amazon Web Services (AWS), including services such as EC2, S3, RDS, EKS, SageMaker, etc.
- Strong proficiency with Git and Git repository management.
- Hands-on experience with Terraform for infrastructure provisioning and management.
- Experience with Helm for deploying and managing applications on Kubernetes.
- Proficiency in scripting languages (e.g., Python, Bash) for automation.
- Excellent problem-solving skills and a strong ability to debug complex issues.
- Strong communication and interpersonal skills to effectively collaborate with cross-functional teams and user-facing interactions.
- Demonstrated ability to take initiative, anticipate needs, and drive projects to completion.
- Ability to thrive in a fast-paced environment and adapt to evolving requirements while adhering to corporate guidelines.
- Ability to write clean code with little syntax/convention feedback.
- Applies software engineering best practices (linting automation, unit testing, documentation, CI/CD).
- Familiarity with modern machine learning methods.
- Knowledge of and experience with high-performance computing, distributed systems, and cloud computing.
Senior Machine Learning Engineer - DevOps
- BS/MS with 3-5 years of industry experience required
- Proven experience in designing, deploying, and managing infrastructure on Amazon Web Services (AWS), including services such as EC2, S3, RDS, EKS, SageMaker, etc.
- Strong proficiency with Git and Git repository management.
- Hands-on experience with Terraform for infrastructure provisioning and management.
- Experience with Helm for deploying and managing applications on Kubernetes.
- Proficiency in scripting languages (e.g., Python, Bash) for automation.
- Excellent problem-solving skills and a strong ability to debug complex issues.
- Strong communication and interpersonal skills to effectively collaborate with cross-functional teams and user-facing interactions.
- Demonstrated ability to take initiative, anticipate needs, and drive projects to completion.
- Ability to thrive in a fast-paced environment and adapt to evolving requirements while adhering to corporate guidelines.
- Ability to write clean code with little syntax/convention feedback.
- Applies software engineering best practices (linting automation, unit testing, documentation, CI/CD).
- Familiarity with modern machine learning methods.
- Knowledge of and experience with high-performance computing, distributed systems, and cloud computing.
Preferred
- Experience with MLOps platforms and tools.
- Familiarity with CI/CD pipelines for ML workflows.
- Knowledge of monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack)
Relocation benefits are NOT available for this job posting
The expected salary range for this position based on the primary location of California for the Machine Learning Engineer is $147,600, - $274,000 and New York is $141,100 - $262,100, and the Senior Machine Learning Engineer for California is $167,400 - $310,800 and New York is $160,100 - $297,300. 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
#tech4lifeAI
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.
Salary Context
This $141K-$310K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Genentech, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($225K) sits 7% above the category median. Disclosed range: $141K to $310K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Genentech AI Hiring
Genentech has 12 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Product Manager. Positions span South San Francisco, CA, San Francisco, CA, US, New York, NY, US. Compensation range: $247K - $312K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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