Interested in this AI/ML Engineer role at Exponent?
Apply Now →Skills & Technologies
About This Role
About Exponent
------------------
Exponent is the only premium engineering and scientific consulting firm with the depth and breadth of expertise to solve our clients’ most profoundly unique, unprecedented, and urgent challenges.
Our vision is to engage multidisciplinary teams of science, engineering, and regulatory experts to empower clients with solutions that create a safer, healthier, more sustainable world. For over five decades, we've connected the lessons of past failures with tomorrow's solutions to advise clients as they innovate technologically complex products and processes, ensure the safety and health of their users, and address the challenges of sustainability.
Join our team of experts with degrees from top programs at over 500 universities and extensive experience spanning a variety of industries. At Exponent, you’ll contribute to the diverse pool of ideas, talents, backgrounds, and experiences that drives our collaborative teamwork and breakthrough insights. Plus, we help you grow your career through mentoring, sponsorship, and a culture of learning. Thanks for your interest in joining our team! Key statistics:* 950\+ Consultants
- 640\+ Ph.D.s
- 90\+ Disciplines
- 30\+ Offices globally
Our Opportunity
-------------------
We are currently seeking a Machine Learning Engineer for our Mechanical Engineering Practice in Menlo Park, CA. In this role, you will be expected to grow in the key aspects of consulting, including technical expertise and effective spoken and written communications, to develop a consulting practice serving clients both within and outside the firm.
You will be responsible for
-------------------------------
- Communicating effectively with other engineers, managers, clients, and administrative reports
- Performing hands\-on work in laboratory environments
- Performing field inspections
- Performing first\-principle analytical calculations
- Performing numerical simulations using physics engines
- Performing modelling work using classical machine learning and deep learning models
- Performing model evaluation and explanation using data science principles
- Creating written reports and presentations
You will have the following skills and qualifications
---------------------------------------------------------
- Ph.D. in Mechanical Engineering, Aerospace Engineering, Systems Engineering, and/or similar fields. This is a full\-time position, and we are unable to consider candidates that will not be finished with all Ph.D. level commitments before beginning employment with Exponent.
- Specialized knowledge in dynamic systems, robotics, physics engine, data science, machine learning, and foundation AI models
- Proficiency in Python
- Good to have working knowledge in version control, software engineering best practices, software development in lakehouses and/or cloud environments
- Excellent verbal and written communications skills
- Excellent project ownership characteristics
- Ability to work within project teams with a strong desire to contribute, have potential for technical and project management leadership, as well as a desire to seek new client relationships to grow a client base
- Must be able to convey technical information to individuals in engineering, business, legal and related industries
- Presently legally authorized to work in the United States. No immigration sponsorship or processing required.
*Applicants are encouraged to submit a CV (Curriculum Vitae) with publications (feel free to include publications that are in review or pending) \[not restricted to 1 page].*
Life @ Exponent
-------------------
To learn more about life at Exponent and our impact, please visit the following links:
https://www.exponent.com/careers/life\-exponent
https://www.exponent.com/company/our\-impact
Attracting, inspiring, developing, and rewarding exceptional people with diverse backgrounds and expertise are central to our corporate culture. Our diverse team allows us to provide better value to our clients and enjoy an enriched work environment.
Our firm is committed to offering a variety of programs and resources to support health and well\-being. We believe that providing competitive benefits as well as compensation and recognition programs empowers our staff to do work that makes a difference.
Work Environment
--------------------
At Exponent, we have found that in\-person interactions deepen employee engagement and are crucial for development, for realizing the full potential of our talented and diverse teams, and for building a more inclusive workplace where all have a sense of belonging. In our offices, you can expect a supportive culture and a collaborative, dynamic, multi\-disciplinary work environment. Our consultants engage in\-person in the office unless they are traveling for client work or other business activities.
We value the rich lives our colleagues enjoy outside of work and understand that work/life balance is critical to our employees and their well\-being. Consultants have the autonomy to balance their work and personal schedules so you can meet with clients, visit inspection sites, attend conferences, and make time for priorities outside of work, too. It is this flexible, agile work style and working hours that allow our teams to drive innovation and results in their own ways, while meeting the needs of clients. \#LI\-Onsite
Compensation
----------------
Our consultants are rewarded for their technical and business contributions and have an opportunity to plan for future success and career growth. Exponent's total compensation plan is consistent with its expectations of the quality and quantity of work performed and with the professional standards set by Exponent. At the Associate and Senior Associate level, total compensation includes base salary, bi\-weekly bonuses for high\-intensity efforts, annual bonus and 401(k) employer contribution of 7% of base salary.
The base salary range for this position is dependent on experience and capabilities which will be assessed during the interview process.
Salary Range
----------------
USD $140,000\.00 \- USD $160,000\.00 /Yr.
Benefits you will enjoy
---------------------------
Access benefits information on our Life@Exponent page:
https://www.exponent.com/careers/life\-exponent
Exponent is a proud equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, veteran status, disability, sexual orientation, gender identity, or any other protected status.
If you need assistance or accommodation due to a disability, you may email us at HR\-Accommodations@exponent.com.
Sign in to Vimeo
====================
This video is private. Sign in to watch.Sign in
Job Locations
-----------------
US\-CA\-Menlo Park
Salary Context
This $140K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Exponent, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($150K) sits 31% below the category median. Disclosed range: $140K to $160K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Exponent AI Hiring
Exponent has 2 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer. Positions span New York, NY, US, Menlo Park, CA, US. Compensation range: $140K - $160K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.