Location:

Santa Clara, California, United States

Job ID:

R0115118

Date Posted:

2025-12-12

Company Name:

HITACHI AMERICA, LTD.

Profession (Job Category):

Engineering & Science

Job Schedule: 

Full time

Remote:

No

Job Description:

Title: Machine Learning Research Scientist

Company: Hitachi America, Ltd.

Division: Research & Development

Location: Santa Clara, CA

Status: Regular, Full Time

The Company

About Hitachi America, Ltd.

Hitachi America, Ltd. is the regional headquarters for Hitachi Group companies in the Americas, overseeing the U.S., Canada, and Latin America markets. Headquartered in Santa Clara, California, Hitachi Americas has been deeply committed to its Social Innovation Business since its establishment in 1959, addressing society's most pressing challenges through innovative solutions. Leveraging its expertise and resources, Hitachi Americas and its subsidiary companies operate across diverse sectors, including transportation, energy, mobility, industrial manufacturing, healthcare, digital engineering, data analytics and others. Driven by Digital, Green, and Innovation, Hitachi Americas remains at the forefront of pioneering solutions that shape the future. For further details, please visit https://www.hitachi.us/.

Through its Social Innovation Business (SIB) that brings together IT, OT (Operational Technology) and products, Hitachi contributes to a harmonized society where the environment, wellbeing, and economic growth are in balance. Hitachi operates globally in four sectors – Digital Systems & Services, Energy, Mobility, and Connective Industries – and the Strategic SIB Business Unit for new growth businesses. With Lumada at its core, Hitachi generates value from integrating data, technology and domain knowledge to solve customer and social challenges. Revenues for FY2024 (ended March 31, 2025) totaled 9,783.3 billion yen, with 618 consolidated subsidiaries and approximately 280,000 employees worldwide. Visit us at www.hitachi.com.

Summary

We are seeking a Machine Learning Research Scientist (Applied RL and Controls) to

develop and prototype learning-based methods for industrial controls and power/energy

applications. This role focuses on reinforcement learning (RL) and deep learning for

nonlinear dynamical systems, with additional emphasis on physics-informed neural

networks (PINNs) and system identification (SysID) to improve data efficiency, robustness,

and deployment reliability.

You will collaborate with researchers and engineers to translate state-of-the-art methods into working prototypes and internal tools that support real-world control systems.

Job Responsibilities

• Design, implement, and evaluate deep learning models for industrial control and power systems applications.

• Develop and benchmark RL and imitation learning approaches for decision-making and optimization in nonlinear, constrained dynamical systems.

• Apply system identification methods to infer model parameters/structure from operational and experimental data to support controller design and validation.

• Use physics-informed learning (PINNs and hybrid physics + neural approaches) to incorporate governing dynamics into training objectives and improve generalization under limited/noisy data.

• Define offline metrics, evaluation protocols, and ablation studies to quantify robustness, stability, and sample efficiency.

• Collaborate with cross-functional teams (controls, domain engineering, software) to integrate models into simulation and experimental pipelines.

• Communicate results via technical reports, presentations, and contributions to publications and patents as appropriate.

• Write clean, reproducible research code and participate in code reviews

A Day in the Life

• Align with technical leads on project goals, milestones, and success metrics.

• Prototype RL agents or learning-based controllers on industrial control scenarios.

• Build SysID and physics-informed workflows to improve model fidelity and controller reliability.

• Review current literature and translate relevant techniques into testable prototype

Requirements

Basic Qualifications

• PhD in Electrical Engineering, Mechanical Engineering, Computer Science, Robotics, or related field.

• Strong Python programming skills and experience building machine learning training and evaluation pipelines.

• Solid fundamentals in control systems and dynamical systems, especially nonlinear systems.

• Experience with deep learning and at least one of: reinforcement learning, imitation learning, system identification, physics-informed neural networks.

• Familiarity with an ML framework (preferably PyTorch).

• Strong communication skills and ability to collaborate across disciplines.

Preferred Qualifications

• Experience applying RL to continuous-control problems with constraints, safety, or stability considerations.

• Experience combining physical modeling with learning (hybrid models, physics-based losses, or differentiable simulation components).

• Experience with simulation environments, digital twins, or controls development toolchains.

• Track record of publications, open-source contributions, patents, or applied research prototypes.

• Exposure to LangChain and/or LangGraph (preferred, not required).

• Experience working with large-scale time-series datasets from industrial system.

Our Values

We are proud to say we are an equal opportunity employer and welcome all applicants for employment without attention to any factor that doesn’t impact your ability to do the job, including race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. We are proud of Japanese heritage, with our values expressed through the Hitachi Spirit:

Wa – Harmony, Trust, Respect

Makoto – Sincerity, Fairness, Honesty, Integrity

Kaitakusha-Seishin – Pioneering Spirit, Challenge

Equal Opportunity Employer (EOE)-Females/Minorities/Protected Veterans/Individuals with Disabilities

If you need a reasonable accommodation to apply for a job at Hitachi, please send the nature of request and contact information to [email protected]. Queries other than accommodation requests will not be responded to.