Interested in this AI/ML Engineer role at AVEVA?
Apply Now →About This Role
AVEVA is creating software trusted by over 90% of leading industrial companies.
Salary Range:
$293,500\.00 \- $489,500\.00This pay range represents the minimum and maximum compensation that the position offers, and final compensation can vary within the range depending on work location, job experience, skills, and relevant educational attainment and/or training.
Job Title: Fellow, AI Investigation \& Incubation
Location: San Leandro, CA (Hybrid)
Employment Type: Full\-time
Job Summary: We are seeking a visionary and strategic AI leader to join our team. This is a high\-impact, VP\-level leadership role within the Chief Technologist organization, focused on pioneering the future of AI at AVEVA. In this role, you will lead the exploration and early\-stage development of artificial intelligence capabilities that will shape the future of AVEVA’s products, platforms, and customer value. You will operate at the forefront of AI research and incubation, bringing a deep understanding of AI model architectures and the ability to critically evaluate emerging technologies beyond surface\-level synthesis—guiding their evolution into scalable, real\-world solutions.
Key Responsibilities:
- Lead AI Investigation Strategy: Establish and execute a structured approach to identifying and evaluating emerging AI technologies, research directions, and architectural patterns relevant to AVEVA’s strategic priorities.
- Prototype \& Validate Novel AI Capabilities: Design and oversee rapid, lightweight experiments and pilots to assess feasibility, value potential, and integration pathways.
- Incubate High\-Impact Concepts: Transition promising AI capabilities into structured incubation tracks with cross\-functional input, ensuring technical maturity and business relevance.
- Drive Technical Clarity: Ensure high standards of rigor in model selection, data use, and evaluation practices across investigations and experiments.
- Cross\-Functional Collaboration: Work closely with cross\-functional teams, including emerging tech innovation, engineering, product management, and business development, to integrate technology trends into strategic plans.
- Leadership: Build a team of and provide leadership and mentorship to AI technologists. Foster a culture of innovation and continuous improvement.
Qualifications:
- Education: Bachelor's degree in AI, Applied Math, Computer Science, or a related field. Advanced degree preferred.
- Experience: Minimum of 10 years of experience in AI/ML\-focused roles spanning applied research, incubation, or innovation within complex organizations. Track record of contributions to research publications and technical conferences highly preferred.
- Knowledge: In\-depth and up\-to\-date understanding of cutting\-edge AI capabilities, including foundation models, relevant to industrial applications as well as best\-practices around AI solution development. Expert knowledge of the AI ecosystem including open source and hosted models.
- Industry Experience: Prior experience building AI solutions for one or more of AVEVA’s key industries including: data centers, pharmaceuticals/life sciences, energy, manufacturing, renewables, and power systems.
- Skills: Strong analytical and problem\-solving skills. Excellent communication with the ability to influence technical and non\-technical stakeholders.
- Attributes: Innovative thinker with a proactive approach. Curious, research\-driven mindset with a critical eye for separating hype from substance. Ability to work independently and collaboratively as part of a team.
- Leadership Experience: Proven experience working with and presenting to senior leadership and executives. Ideal candidates will have prior management experience and demonstrated ability to recruit, mentor, and lead a distributed team.
R\&D at AVEVA
Our global team of 2000\+ developers work on an incredibly diverse portfolio of over 75 industrial automation and engineering products, which cover everything from data management to 3D design. AI and cloud are at the centre of our strategy, and we have over 150 patents to our name.
Our track record of innovation is no fluke – it’s the result of a structured and deliberate focus on learning, collaboration and inclusivity. If you want to build applications that solve big problems, join us.
Find out more: aveva.com/en/about/careers/r\-and\-d\-careers/
USA Benefits include:
Flex work hours, 20 days PTO rising to 25 with service, three paid volunteering days, primary and secondary parental leave, well\-being support, medical, dental, vision, and 401K.
It’s possible we’re hiring for this position in multiple countries, in which case the above benefits apply to the primary location. Specific benefits vary by country, but our packages are similarly comprehensive.
Find out more: aveva.com/en/about/careers/benefits/
Hybrid working
We work in a hybrid way at AVEVA. Most roles are based at a local AVEVA office, with an expectation of being on\-site 50% of your working hours to support collaboration and connection. Some positions are fully office\-based depending on the nature of the work, and certain roles that support specific customers or markets may be remote. The working arrangement for this position will be confirmed during the hiring process.
Hiring process
Interested? Great! Get started by submitting your cover letter and CV through our application portal. AVEVA is committed to recruiting and retaining people with disabilities. Please let us know in advance if you need reasonable support during your application process.
Find out more: aveva.com/en/about/careers/hiring\-process
About AVEVA
AVEVA is a global leader in industrial software with more than 6,500 employees in over 40 countries. Our cutting\-edge solutions are used by thousands of enterprises to deliver the essentials of life – such as energy, infrastructure, chemicals, and minerals – safely, efficiently, and more sustainably.
We are committed to embedding sustainability and inclusion into our operations, our culture, and our core business strategy. Learn more about how we are progressing against our ambitious 2030 targets: sustainability\-report.aveva.com/
Find out more: aveva.com/en/about/careers/
AVEVA requires all successful applicants to undergo and pass a drug screening and comprehensive background check before they start employment. Background checks will be conducted in accordance with local laws and may, subject to those laws, include proof of educational attainment, employment history verification, proof of work authorization, criminal records, identity verification, credit check. Certain positions dealing with sensitive and/or third\-party personal data may involve additional background check criteria.
AVEVA is an Equal Opportunity Employer. We are committed to being an exemplary employer with an inclusive culture, developing a workplace environment where all our employees are treated with dignity and respect. We value diversity and the expertise that people from different backgrounds bring to our business. AVEVA provides reasonable accommodation to applicants with disabilities where appropriate. If you need reasonable accommodation for any part of the application and hiring process, please notify your recruiter. Determinations on requests for reasonable accommodation will be made on a case\-by\-case basis.
Salary Context
This $293K-$489K 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 AVEVA, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($391K) sits 82% above the category median. Disclosed range: $293K to $489K.
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.
AVEVA AI Hiring
AVEVA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Leandro, CA, US. Compensation range: $489K - $489K.
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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