Overview
At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget.
Our Science & Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus.
The Physical and Computational Sciences Directorate's (PCSD's) strengths in experimental, computational, and theoretical chemistry and materials science, together with our advanced computing, applied mathematics and data science capabilities, are central to the discovery mission we embrace at PNNL. But our most important resource is our people-experts across the range of scientific disciplines who team together to take on the biggest scientific challenges of our time.?
The Advanced Computing, Mathematics, and Data Division (ACMDD) focuses on basic and applied computing research encompassing artificial intelligence,?applied mathematics, computing technologies, and data and computational engineering. Our scientists and engineers apply end-to-end co-design principles to advance future energy-efficient computing systems and design the next generation of algorithms to analyze, model, understand, and control the behavior of complex systems in science, energy, and national security.
Responsibilities
The Data Sciences and Machine Intelligence Group in ACMDD at PNNL seeks a Data Scientist to join the group to lead and support scientific research in robotics, autonomous systems, and intelligent control. This is an excellent opportunity to contribute to cutting-edge research in robotic autonomy, learning-enabled control, and embodied AI. You will join a multi-disciplinary team advancing scientific discovery through intelligent systems and autonomous laboratories, while helping develop new capabilities in optimization, machine learning, and integration.
The primary focus of this senior scientist position will be to grow existing, and adding new, capabilities in the areas of Optimization, Robotics, and Artificial Intelligence, and to help strengthen the group's leadership in data science and machine intelligence fields. A successful candidate should have shown significant national level expertise in one or more of the following technical areas: Design and integration of robotics systems, optimization and optimization-based decision-making, artificial intelligence and machine learning, autonomous control and decision systems, model predictive control, reinforcement learning algorithms, deploying machine learning using cloud and edge computing solutions, transformer architectures for time series analysis.
As a researcher in robotics and autonomous systems at PNNL, you will contribute to the development of intelligent, integrated robotic platforms for scientific applications such as autonomous laboratories. Your work will support PNNL's mission to accelerate scientific discovery through automation, modeling, and machine intelligence. You will contribute to software development and applied mathematics research in robotics for scientific applications such as autonomous laboratories. The emphasis will be given to modeling, simulation, system integration, and control of heterogeneous robotics systems and multiagent systems. You will develop and apply advanced algorithms for motion planning, learning-enabled control, and autonomous decision-making using techniques such as model predictive control (MPC), control barrier functions (CBFs), differentiable predictive control, and reinforcement learning. You will also explore cutting-edge topics such as robotic manipulation, Sim2Real transfer, vision-language-action models, and foundation models for robotics, helping to drive robust task learning and generalization across physical and simulated platforms. A key part of your role will involve contributing to high-quality software development, including the use of physics-based simulators (e.g., MuJoCo, IsaacSim, Gazebo), ROS, and machine learning frameworks (e.g., PyTorch, JAX, TensorFlow). You will help design and maintain codebases that support scalable experimentation, dataset acquisition, and integration of ML-enabled control systems in both simulated and physical testbeds.
You will also be expected to summarize technical findings, prepare and contribute to peer-reviewed publications, and present results in leading conferences and journals. Collaboration is central to this role. You will work closely with a diverse team of computer scientists, engineers, mathematicians, and domain experts. You are also expected to mentor post-doctoral researchers, and seek programmatic funding. As such, excellent communication and interpersonal skills are essential for engaging in interdisciplinary research and delivering impactful scientific outcomes.
Designs, develops, and implements methods, processes, and systems to analyze diverse data.
Applies knowledge of statistics, machine learning, advanced mathematics, simulation, software development, and data modeling to integrate and clean data, recognize patterns, address uncertainty, pose questions, and make discoveries from structured and/or unstructured data.
Produces solutions driven by exploratory data analysis from complex and high-dimensional datasets.
Designs, develops, and evaluates predictive models and advanced algorithms that lead to optimal value extraction from the data.
Demonstrates ability to transfer skills across application domains.
This position is based at the PNNL main campus in Richland, WA and requires onsite work.
Qualifications
Minimum Qualifications:
BS/BA and 5+ years of relevant experience -OR-
MS/MA and 3+ years of relevant experience -OR-
PhD with 1+ year of relevant experience
Preferred Qualifications:
PhD or MS in Robotics, Computer Science, Applied Mathematics, Electrical Engineering, Mechanical/Aerospace Engineering, or related scientific fields.
Hands-on experience in robot learning, motion planning, navigation, and control using both classical and modern control methods (e.g., MPC, PID, LQR) and modern machine learning techniques (e.g., reinforcement learning, imitation learning, computer vision).
Experience leading or contributing to R&D proposals for federal agencies (e.g., DOE, DARPA, NSF), with a history of successful funding as PI or co-PI.
Deep understanding of robot kinematics, dynamics, and sensor integration and perception pipelines.
Familiarity with multimodal perception and embodied AI for safe @context-aware autonomous decision-making.
Experience with physics-simulation frameworks such as MuJoCo, Gazebo, and IsaacSim.
Strong programming skills in Python/Julia/C++, ROS, and machine learning frameworks (e.g., PyTorch, JAX, TensorFlow).
Experience working with robotic systems such as manipulators, mobile robots, autonomous vehicles, or similar platforms.
Experience using machine learning in cloud environments (such as Google Cloud and AWS) and edge hardware for real-time deployment and scalability.
Experience with modern scientific deep learning methods (e.g., Neural ODEs, PINNs, Operator networks, Hamiltonian and Lagrangian neural networks, graph neural networks).
The ability to develop and evaluate integrated systems.
Demonstrated leadership in delivering complex, end-to-end robotics solutions with successful Sim2Real transfer for various tasks and automated workflows.
Prior experience mentoring early career staff, guiding multidisciplinary teams, and shaping research vision and technical roadmaps.
Proven track record of impactful results, evidenced by successful projects, fellowships, grants, patents, and publications in top robotics and AI conferences/journals (e.g., ICRA, RSS, IROS, CoRL, ACC, CDC, NeurIPS, ICML, CVPR).
Ability to architect and evaluate integrated robotic systems across simulation and real-world environments.
Hazardous Working Conditions/Environment
Not Applicable
Testing Designated Position
This is not a Testing Designated Position (TDP).
About PNNL
Pacific Northwest National Laboratory (PNNL) is a world-class research institution powered by a highly educated, diverse workforce committed to the values of Integrity, Creativity, Collaboration, Impact, and Courage. Every year, scores of dynamic, driven people come to PNNL to work with renowned researchers on meaningful science, innovations and outcomes for the U.S. Department of Energy and other sponsors; here is your chance to be one of them!
At PNNL, you will find an exciting research environment and excellent benefits including health insurance, and flexible work schedules. PNNL is located in eastern Washington State-the dry side of Washington known for its stellar outdoor recreation and affordable cost of living. The Lab's campus is only a 45-minute flight (or ~3 hour drive) from Seattle or Portland, and is serviced by the convenient PSC airport, connected to 8 major hubs.
Commitment to Excellence and Equal Employment Opportunity
Our laboratory is committed to fostering a work environment where all individuals are treated with fairness and respect while solving critical challenges in fundamental sciences, national security, and energy resiliency. We are an Equal Employment Opportunity employer.
Pacific Northwest National Laboratory (PNNL) is an Equal Opportunity Employer. PNNL considers all applicants for employment without regard to race, religion, color, sex, national origin, age, disability, genetic information (including family medical history), protected veteran status, and any other status or characteristic protected by federal, state, and/or local laws.
We are committed to providing reasonable accommodations for individuals with disabilities and disabled veterans in our job application procedures and in employment. If you need assistance or an accommodation due to a disability, contact us at careers@pnnl.gov .
Drug Free Workplace
PNNL is committed to a drug-free workplace supported by Workplace Substance Abuse Program (WSAP) and complies with federal laws prohibiting the possession and use of illegal drugs.
If you are offered employment at PNNL, you must pass a drug test prior to commencing employment. PNNL complies with federal law regarding illegal drug use. Under federal law, marijuana remains an illegal drug. If you test positive for any illegal controlled substance, including marijuana, your offer of employment will be withdrawn.
Security, Credentialing, and Eligibility Requirements
As a national laboratory, PNNL is responsible for adhering to the Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which require new employees to obtain and maintain a HSPD-12 Personal Identify Verification (PIV) Credential. To obtain this credential, new employees must successfully complete the applicable tier of federal background investigation post hire and receive a favorable federal adjudication. The tier of federal background investigation will be determined by job duties and national security or public trust responsibilities associated with the job. All tiers of investigation include a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last 1 to 7 years (depending on the applicable tier of investigation). Illegal drug activities include marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.
For foreign national candidates:
If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) Federal risk determination to maintain employment. Once you meet the three-year residency requirement thereafter, you will be required to obtain a PIV credential to maintain employment. The tier of federal background investigation required to obtain the PIV credential will be determined by job duties at the time you become eligible for the PIV credential.
Mandatory Requirements
Please be aware that the Department of Energy (DOE) prohibits DOE employees and contractors from having any affiliation with the foreign government of a country DOE has identified as a "country of risk" without explicit approval by DOE and Battelle. If you are offered a position at PNNL and currently have any affiliation with the government of one of these countries, you will be required to disclose this information and recuse yourself of that affiliation or receive approval from DOE and Battelle prior to your first day of employment.
Rockstar Rewards
Employees and their families are offered medical insurance, dental insurance, vision insurance, robust telehealth care options, several mental health benefits, free wellness coaching, health savings account, flexible spending accounts, basic life insurance, disability insurance, employee assistance program, business travel insurance, tuition assistance, relocation, backup childcare, legal benefits, supplemental parental bonding leave, surrogacy and adoption assistance, and fertility support. Employees are automatically enrolled in our company-funded pension plan and may enroll in our 401 (k) savings plan with company match*. Employees may accrue up to 120 vacation hours per year and may receive ten paid holidays per year.
Research Associates excluded.
**All benefits are dependent upon eligibility.
Click Here For Rockstar Rewards (https://careers.pnnl.gov/rockstar-rewards)
Notice to Applicants
PNNL lists the full pay range for the position in the job posting. Starting pay is calculated from the minimum of the pay range and actual placement in the range is determined based on an individual's relevant job-related skills, qualifications, and experience. This approach is applicable to all positions, with the exception of positions governed by collective bargaining agreements and certain limited-term positions which have specific pay rules.
As part of our commitment to fair compensation practices, we do not ask for or consider current or past salaries in making compensation offers at hire. Instead, our compensation offers are determined by the specific requirements of the position, prevailing market trends, applicable collective bargaining agreements, pay equity for the position @type, and individual qualifications and skills relevant to the performance of the position.
Minimum Salary
USD $140,200.00/Yr.
Maximum Salary
USD $228,800.00/Yr.
