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About This Role
SUMMARY/JOB PURPOSE
The Engineer II - AI and Agentic is responsible for designing, building, and deploying production-grade agentic applications that automate multi-step IT and business workflows across Exelixis. This role delivers spec-driven, testable, and secure AI solutions, contributes to platform patterns and reusable components, while advancing the engineering standards.
ESSENTIAL DUTIES/RESPONSIBILITIES
- Design and deliver end-to-end agentic applications and multi-agent workflows that automate IT and business processes.
- Convert prototypes into hardened, production-ready services with clear specifications, modular design, robust error handling, and comprehensive test coverage.
- Integrate LLMs, prompts, tools, APIs, and enterprise data sources (e.g., Bedrock, Databricks Mosaic AI, MCP servers) into reliable use cases.
- Implement CI/CD pipelines, automated evaluation harnesses, observability, and artifact versioning for AI/agent workloads.
- Author architecture notes, runbooks, SOPs, and technical documentation to support adoption, operability, and maintainability.
- Lead prompt/spec reviews, code reviews, threat modeling exercises, and release-readiness gates.
- Apply and evangelize secure-by-design and responsible AI principles, including data governance, privacy, and safe prompting aligned to organizational policy
- Contribute reusable components, patterns, and internal libraries to the platform
- Partner with product managers, security, and platform teams to translate business needs into scalable technical solutions
- Performs other duties as assigned
- Complies with all policies and standards
SUPERVISORY RESPONSIBILITIES
- No supervisory responsibilities
EDUCATION/EXPERIENCE/KNOWLEDGE/SKILLS & ABILITIES
Education
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field and 5 years of related experience; or
- Master’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field and 3 year or related experience; or
- Equivalent combination of education and experience.
Experience
- 2 - 4 years of relevant experience in AI application, data science, automation, or related technical work, including hands-on experience building or contributing to LLM-enabled applications, agentic workflows, API integrations.
- Strong hands-on experience building AI-enabled applications and agentic workflows on top of enterprise data platforms.
- Deep understanding of modern data and analytics ecosystems, including data engineering, metadata, governance, and platform integration patterns.
- Ability to translate business and analytical use cases into scalable, production-ready AI and data solutions.
- Experience working across AI, data, platform, and security teams to establish reusable patterns, standards, and best practices.
Knowledge, Skills and Abilities
Required:
- Intermediate to Advanced proficiency in Python and/or JavaScript/TypeScript, including modular design, testing, and packaging.
- Intermediate proficiency with agent frameworks and agentic patterns: tool use, planning/execution loops, guardrails, evaluation, and prompt/version management.
- Intermediate proficiency building and consuming REST APIs, and integrating enterprise systems with OAuth/JWT, secrets management, and secure data handling.
- Intermediate proficiency with CI/CD, automated testing (unit/integration), linting, and observability for AI applications.
- Intermediate understanding of security best practices, architecture design principles, and responsible AI/data governance.
- Intermediate communication skills, written and verbal, and demonstrated ability to collaborate with cross-functional stakeholders and mentor junior engineers.
- Intermediate experience with cloud AI platforms — preferably AWS Cloud, AWS Bedrock, and Databricks Mosaic AI — and vector stores or retrieval-augmented generation (RAG) patterns.
- Intermediate knowledge of AI-native engineering using Claude Code, Codex, GitHub Copilot, or equivalent coding agents.
Preferred:
- Experience delivering evaluation frameworks, red-teaming, or LLM observability tooling.
Travel Requirements
- No travel is required for this position.
#LI-EZ1
Location: Alameda, CA Range for this position: $170,000 - $215,000 The base pay offered will take into account 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 $170K-$215K range is above the median 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 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($192K) sits 9% below the category median. Disclosed range: $170K to $215K.
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.
Exelixis AI Hiring
Exelixis has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Alameda, CA, US, Alameda, CA. Compensation range: $215K - $215K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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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