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About This Role
SUMMARY/JOB PURPOSE
The Director, Engineering AI and Agentic leads the strategy, architecture, delivery, and operations of enterprise AI platforms and AI\-enabled business solutions. This role combines deep AI engineering leadership with strong product management capabilities to translate business needs into scalable, secure, measurable, and user\-centered AI products. The Director, Engineering AI And Agentic is accountable for building reusable AI platform capabilities, delivering production\-grade agentic and generative AI solutions, advancing AI\-native engineering practices, and partnering across IT, security, data, legal, compliance, and business stakeholders to responsibly scale AI adoption across Exelixis.
ESSENTIAL DUTIES/RESPONSIBILITIES
- Define and execute the AI engineering strategy, roadmap, and operating model for enterprise AI platforms, reusable services, and AI\-enabled business solutions.
- Lead the design and delivery of scalable AI platform capabilities, including model access patterns, agent orchestration, retrieval\-augmented generation, evaluation frameworks, prompt and artifact management, observability, governance controls, and reusable integration patterns.
- Oversee end\-to\-end development of production\-grade generative AI, agentic AI, and automation solutions that improve productivity, decision support, operational efficiency, and business outcomes across functions.
- Apply strong product management practices, including opportunity assessment, stakeholder discovery, prioritization, roadmap planning, user experience definition, value measurement, adoption planning, and lifecycle management.
- Partner with business leaders, product managers, architects, data teams, cybersecurity, privacy, legal, compliance, and enterprise application teams to translate strategic business needs into secure, governed, and scalable AI solutions.
- Establish AI\-native engineering standards and practices, including specification\-driven development, agent\-assisted software delivery, automated testing, code quality, reusable patterns, DevSecOps, CI/CD, release management, and operational support models.
- Ensure AI solutions are designed for reliability, security, scalability, auditability, explainability, human oversight, measurable autonomy, and responsible AI use consistent with company policies and regulatory expectations.
- Manage platform, and technology decisions across AI providers, cloud services, data platforms, development tools, and open\-source components; assess build\-versus\-buy options and total cost of ownership.
- Create and monitor success metrics for AI platforms and solutions, including adoption, productivity impact, quality, latency, cost, risk reduction, reuse, user satisfaction, and business value realization.
- Lead architecture reviews, prompt and solution reviews, security reviews, model and agent evaluations, release\-readiness assessments, and post\-production performance monitoring.
- Develop talent, delivery practices, technical documentation, playbooks, reference architectures, and enablement materials that help teams adopt AI\-native engineering safely and effectively.
- Performs other duties as assigned
- Complies with all policies and standards
SUPERVISORY RESPONSIBILITIES
- Recruit staff to meet the unit’s immediate objectives and develop staff to meet longer term objectives.
- Appraise the performance of subordinates and recommend changes regarding job, pay and employment status.
- Assist with operating budgets, and capital budgets if required, and control expenses to adhere to approved budgets.
- Keep informed of company policies and procedures as well as keep current with technical and regulatory developments to ensure compliance.
- Develop policies and procedures or recommend revisions to facilitate the accomplishment of the unit’s objectives.
- Cooperate with management and staff of other organizational units to accomplish overall organizational objectives.
- Provide a safe workplace and ensure employees are aware of and adhere to safety policies and procedures.
EDUCATION/EXPERIENCE/KNOWLEDGE/SKILLS \& ABILITIES
Education
- Bachelor’s degree in a related discipline and 13 years of related experience; or
- Master’s degree in a related discipline and 11 years of related experience; or
- Equivalent combination of education and experience.
Experience
- 5\+ years in AI Engineering or Cloud platform Engineering
- Leadership experience, overseeing cross\-functional teams
- Experience in full lifecycle system development (design, implementation, validation and support) of cloud based solutions and platforms
Knowledge, Skills and Abilities
Required:
- Advanced understanding of AI engineering, generative AI, agentic AI patterns, AI platform architecture, software engineering, cloud\-native design, enterprise integration, and production operations.
- Strong ability to define platform strategy, product roadmaps, business cases, success metrics, delivery plans, and adoption approaches for enterprise AI capabilities.
- Hands\-on technical credibility with modern AI solution patterns, including LLMs, prompt/context engineering, tool use, multi\-step workflows, agent orchestration, retrieval\-augmented generation, vector search, evaluation, monitoring, and guardrails.
- Experience with cloud AI and data platforms such as AWS, Amazon Bedrock, Databricks Mosaic AI, or equivalent technologies; familiarity with APIs, event\-driven architectures, data pipelines, identity, secrets management, and enterprise security patterns.
- Demonstrated ability to lead AI\-native engineering practices using coding agents, AI\-assisted development tools, specification\-driven development, automated testing, CI/CD, DevSecOps, observability, and continuous improvement.
- Strong understanding of responsible AI, data governance, privacy, cybersecurity, model risk, auditability, human\-in\-the\-loop controls, regulatory expectations, and enterprise policy compliance.
- Excellent leadership skills with the ability to recruit, mentor, coach, and develop diverse technical teams while setting clear standards for quality, accountability, innovation, and delivery excellence.
- Strong communication, executive presence, facilitation, stakeholder management, and influencing skills; able to translate complex AI concepts into business implications, risks, tradeoffs, and decisions.
- Ability to work effectively in ambiguous and fast\-changing environments, make informed tradeoffs, resolve conflicts, and align cross\-functional teams around outcomes.
- Advanced planning, portfolio management, vendor management, financial management, and execution skills with the ability to manage multiple initiatives across a matrixed organization.
- Knowledge of life sciences, biotechnology, clinical, regulatory, GxP, SOX, 21 CFR Part 11, or validated system environments is preferred.
Travel Requirements
- 10% This position will require up to 10% of travel time.
\#LI\-EZ1
Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The base pay range for this position is $215,000 \- $306,000 annually. The base pay range may take into account the candidate’s geographic region, which will adjust the pay depending on the specific work location. The base pay offered will take into account the candidate’s geographic region, job\-related knowledge, skills, experience and internal equity, among other factors.
In addition to the base salary, as part of our Total Rewards program, Exelixis offers comprehensive employee benefits package, including a 401k plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts. Employees are also eligible for a discretionary annual bonus program, or if field sales staff, a sales\-based incentive plan. Exelixis also offers employees the opportunity to purchase company stock, and receive long\-term incentives, 15 accrued vacation days in their first year, 17 paid holidays including a company\-wide winter shutdown in December, and up to 10 sick days throughout the calendar year.
If you have a disability and need an accommodation in relation to the recruiting process, please email us at: *[email protected]**.*
WORKING CONDITIONS:
Our office is a modern space that fosters collaboration and creativity. Teams work closely together, sharing ideas and solutions in a supportive atmosphere. We provide all necessary equipment, including dual monitors and ergonomic chairs, to ensure a comfortable workspace.
DISCLAIMER:
The preceding job description has been designed to indicate the general nature and level of work performed by employees within this classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to the job.
*We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.*
Salary Context
This $215K-$306K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Exelixis, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($260K) sits 21% above the category median. Disclosed range: $215K to $306K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Exelixis AI Hiring
Exelixis has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Alameda, CA, US. Compensation range: $145K - $306K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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