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### The Position
Why Genentech
We’re passionate about delivering on Our Promise to improve the lives of patients and create healthier communities for all. We foster a culture of inclusivity, integrity and creativity while boldly pursuing answers to the world’s most complex health challenges and transforming society.
Who We Are
Genentech’s Data, Digital, and Analytics (DDA) organization enables data\-driven decision making and scalable digital and AI innovation across Commercial, Medical, and Government Affairs (CMG). Through data productionization, advanced analytics, AI/ML, and digital capabilities, DDA partners across the enterprise to accelerate business impact and improve patient outcomes.
The Data Science and Machine Learning (DSML) organization within DDA focuses on advancing Genentech’s enterprise AI and data science capabilities through scalable product foundations, modern ML/AI technologies, and standardized operational frameworks. DSML partners closely with business and technology organizations to ensure enterprise AI and data science capabilities are scalable, interoperable, operationally sustainable, and aligned to enterprise governance standards.
Job Summary
The Senior Director, Data Science Solutions will lead the strategy, prioritization, and deployment of industry\-validated biopharma data science solutions that accelerate modernization of Genentech’s Commercial Engine and enterprise decision capabilities.
This role will serve as a senior leader within the DSML organization, responsible for identifying, adopting, and operationalizing specialized biopharma data science capabilities that improve the speed and effectiveness of commercial and business operations.
Through strategic external partnerships, this leader and team will establish and leverage industry\-validated data science solutions to:
- rapidly deliver ad hoc data science\-powered advanced analytical capabilities and business insights,
- accelerate adoption and operational integration of enterprise AI products and data science solutions,
- and enable business organizations to effectively utilize modern DS/AI\-enabled decision capabilities at scale.
The position will focus on accelerating adoption of industry\-validated solutions through strategic vendor partnerships, solution prioritization, roadmap development, implementation leadership, and cross\-functional operational integration. This leader will ensure deployed solutions are practical, scalable, interoperable, and aligned with Genentech’s evolving business priorities and enterprise AI ecosystem.
In the near term, this role will provide technical and strategic leadership for vendor\-delivered data science solutions supporting commercial and operational modernization initiatives. Over time, the role will establish scalable implementation models, reusable operational patterns, and sustainable enterprise capabilities that accelerate adoption of modern DS/AI\-enabled business operations across Genentech.
This leader will collaborate extensively across DSML, Insight \& Analytics (I\&A), Data Product Management (DPM), Digital Experiences (DE), Roche Digital \& Technology (RDT), and external strategic partners to drive practical and scalable deployment of data science capabilities across the enterprise.
Key Job Responsibilities
Strategic Leadership \& Commercial Engine Modernization
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- Define and execute the strategy for industry\-validated data science solutions that modernize Genentech’s Commercial Engine and deliver scalable business value.
- Develop solution roadmaps aligned to enterprise priorities, operational needs, vendor capabilities, and technology strategy.
- Drive scalable adoption of industry\-validated data science solutions across commercial and operational workflows.
- Establish scalable approaches to rapidly deliver ad hoc data science\-powered advanced analytical capabilities and decision support solutions to business partners.
- Evaluate external partnership opportunities to accelerate enterprise modernization with industry\-validated data science solutions.
- Partner with senior business and technology leaders to align solution priorities with enterprise strategic objectives.
Vendor Partnership \& Solution Delivery
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- Lead strategic partnerships with external vendors and solution providers delivering industry\-validated AI/ML and data science solutions.
- Evaluate vendor solutions for scalability, operational fit, technical quality, implementation readiness, and enterprise compatibility.
- Provide implementation leadership and technical oversight for externally delivered solutions.
- Partner with procurement, legal, security, and business stakeholders to support effective vendor engagement and delivery execution.
- Ensure external solutions align with enterprise technology, security, and operational standards.
Implementation \& Operational Scaling
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- Lead deployment and operationalization of industry\-validated data science solutions across CMG environments.
- Utilize, and drive cross\-organizational adoption of, implemented data science solutions.
- Establish repeatable implementation patterns and scalable operational models for enterprise capability deployment.
- Partner with business and technology stakeholders to accelerate modernization of operational workflows and decision enablement capabilities.
- Support scalable deployment aligned with enterprise operational and technology requirements.
Cross\-Functional Leadership
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- Partner closely with DSML teams, Insight \& Analytics (I\&A), Data Product Management (DPM), Digital Experiences (DE), and Roche Digital \& Technology (RDT) organizations to align data science solutions with enterprise priorities and technology ecosystems.
- Serve as a strategic advisor across technical and business organizations to drive adoption of established data science solutions.
- Influence senior stakeholders and drive alignment across highly matrixed organizations.
People
- Attract, lead, and develop highly\-connected, highly\-motivated and high\-performing teams.
- Provide guidance, training, and career development opportunities for team members.
- Drive a culture of employee engagement and accountability through performance management, incentive alignment, and rewards and recognition of all team members.
Minimum Qualifications \& Experience
- Master’s degree in Data Science, Statistics, Computer Science, Engineering, Biostatistics, or related quantitative field required; PhD preferred.
- 10\+ years of experience in data science, AI/ML, advanced computational solutions, or related technical disciplines within biotechnology and/or pharmaceutical industries.
- Deep understanding of healthcare data and data\-informed decision processes.
- Proven record in driving successful enterprise operational modernization and deployment of scalable data science and AI/ML capabilities.
- Demonstrated experience leading external vendor partnerships, consulting engagements, or third\-party technology solution implementations.
- Experience leading enterprise\-scale implementation and operationalization of AI/ML and data science capabilities within complex organizations.
- Strong understanding of scalable AI/ML technologies, operational deployment models, and enterprise solution integration approaches.
- Experience working cross\-functionally across business, technology, and operational organizations.
- Proven leadership experience in matrixed organizations with multiple senior stakeholders.
- Excellent communication, stakeholder management, prioritization, and executive influence capabilities.
- Strong leadership and team management abilities, with experience coaching and developing high\-performing teams.
Preferred Qualifications \& Experience
- PhD in Data Science, Statistics, Computer Science, Engineering, Biostatistics, or related field preferred.
- Experience with industry\-validated biopharma data science solution ecosystems and commercial AI/ML enablement platforms.
- Familiarity with modern AI/ML platforms, cloud technologies, enterprise operational deployment frameworks, and scalable enterprise architectures.
- Experience leading vendor\-delivered data science solution implementation roadmaps and enterprise modernization initiatives.
- Demonstrated success accelerating organizational adoption of scalable enterprise AI and data science capabilities.
- Experience balancing strategic innovation with operational execution and enterprise business enablement.
- Experience working within highly matrixed organizations across multiple therapeutic areas or enterprise functions.
Location
- This position is based in South San Francisco, CA.
- Relocation assistance is available.
*The expected salary range for this position based on the primary location of South San Francisco, CA is $255,900 \- $475,200 USD Annual. 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
- LI\-NN2
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.
JOB FACTS
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- ### Job Sub Category
Early Development Science
- ### Schedule
Full time
- ### Job Type
Regular
- ### Posted Date
Jun 12th 2026
- ### Job ID
202606\-114599
Salary Context
This $255K-$475K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 951 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 1,809 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 in Demand for This Role
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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($365K) sits 98% above the category median. Disclosed range: $255K to $475K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Genentech AI Hiring
Genentech has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $475K - $475K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,258 tracked positions. That's 26% 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 1,809 open positions tracked in our dataset. By seniority: 34 entry-level, 797 mid-level, 728 senior, and 250 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (294 positions). The remaining 1,505 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 1,809 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,274), Data Scientist (145), AI Software Engineer (132). 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 (34) are outnumbered by mid-level (797) and senior (728) 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 250 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (294 positions), with 1,505 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (877 postings), Aws (592 postings), Azure (458 postings), Rag (380 postings), Gcp (364 postings), Pytorch (277 postings), Prompt Engineering (266 postings), Claude (250 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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