Interested in this AI/ML Engineer role at Exelixis?
Apply Now →Skills & Technologies
About This Role
SUMMARY/JOB PURPOSE
The Associate Engineer \- AI and Agentics is responsible for building foundational AI\-enabled applications, prototypes, prompts, tests, documentation, and agentic automations while learning SDLC best practices, security expectations, and responsible AI standards.
ESSENTIAL DUTIES/RESPONSIBILITIES
- Assist in building simple AI\-enabled prototypes, internal tools, and workflow automations.
- Translate clearly defined requirements into small software components, scripts, prompts, test cases, and configuration changes.
- Support prompt engineering, basic agent workflows, retrieval\-assisted use cases, and API integrations.
- Use approved AI\-assisted development tools, such as coding copilots or agentic coding tools, to learn, prototype, document, and improve code with appropriate review.
- Write and execute basic unit tests, validation checks, and troubleshooting steps to confirm expected behavior.
- Maintain clear documentation, including setup notes, design summaries, prompt/spec notes, user instructions, and operational runbooks.
- Participate in code reviews, demos, testing sessions, backlog discussions, and team learning activities.
- Follow company policies for data privacy, information security, access control, compliance, and responsible use of AI.
- 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; or
- Equivalent combination of education and experience.
Experience
- Candidates with early experience of software engineering, data science, automation, or related technical work gained through internships, academic projects, research assignments, open\-source contributions, hackathons, or personal portfolio work.
- Portfolio, GitHub repository, class project, internship deliverable, or capstone project that demonstrates coding, automation, AI, data, or problem\-solving skills.
Knowledge, Skills and Abilities
Required:
- Foundational programming ability in Python, JavaScript/TypeScript, Java, or a similar language, demonstrated through coursework or projects.
- Basic understanding of software development fundamentals, including data structures, APIs, Git, debugging, testing, and documentation.
- Introductory knowledge of AI, machine learning, generative AI, LLMs, prompt engineering, or data science concepts through coursework, projects, or self\-directed learning.
- Ability to learn and apply approved AI\-assisted development tools responsibly, with review and coaching from senior team members.
- Ability to quickly acquire new knowledge, apply critical thinking, maintain attention to detail, and ask insightful questions to resolve uncertainty in requirements or technical approaches.
- Clear written and verbal communication skills, including the ability to explain work, document decisions, and collaborate with technical and non\-technical teammates.
- Ability to apply principles of responsible AI, data privacy, information security, and ethical technology practices in day\-to\-day work.
Preferred:
- Basic familiarity with cloud platforms, databases, REST APIs, notebooks, or data tools is preferred.
Travel Requirements
- 0% No travel is expected of this position
\#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 $103,000 \- $145,000 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 $103K-$145K range is in the lower quartile 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. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($124K) sits 42% below the category median. Disclosed range: $103K to $145K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.