Machine Learning Engineer (Mechanical Engineering Practice, Ph.D.)

Exponent•Menlo Park, CA

14h•$140,000 - $160,000•Onsite

About The Position

Exponent is seeking a Machine Learning Engineer for its Mechanical Engineering Practice in Menlo Park, CA. This role involves growing in key consulting aspects, including technical expertise and communication skills, to serve clients both within and outside the firm. The position requires effective communication with engineers, managers, clients, and administrative staff, as well as hands-on work in laboratory and field environments. Responsibilities include performing first-principle analytical calculations, numerical simulations using physics engines, and modeling using classical machine learning and deep learning models. Model evaluation and explanation using data science principles, and the creation of written reports and presentations are also key components of this role.

Requirements

Ph.D. in Mechanical Engineering, Aerospace Engineering, Systems Engineering, and/or similar fields.
Specialized knowledge in dynamic systems, robotics, physics engine, data science, machine learning, and foundation AI models
Proficiency in Python
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.

Nice To Haves

Working knowledge in version control, software engineering best practices, software development in lakehouses and/or cloud environments

Responsibilities

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