AI/ML Engineer – Time Series & Robotics

Are you ready to be part of a company that's not just talking about the future, but energetically shaping it? Join the Microvast Team (NASDAQ: MVST), a technology innovator that designs, develops and manufactures lithium-ion battery solutions. Founded in 2006 and headquartered in Stafford, TX, Microvast is renowned for its cutting-edge cell technology and its vertical integration capabilities which extend from core battery chemistry (cathode, anode, electrolyte, and separator) to battery packs. By integrating the process from raw material to system assembly, Microvast has developed a family of products covering a broad breadth of market applications. More information can be found on the corporate website: www.microvast.com

If you’re ready to be part of a company that’s not just adapting to change, but driving it, Microvast is the place for you. Apply now and electrify your career with a true leader in the global energy transformation.

The AI / ML Engineer will develop and deploy machine learning algorithms that analyze multi-sensor time-series data from vehicles and robotic platforms. This position focuses on building robust models that improve system performance, monitoring, and intelligent behavior, working closely with robotics, controls, and embedded teams.

• Design and implement ML models for time-series sensor data (e.g., currents, torques, IMUs, joint states, vehicle signals, cameras, GPS).
• Build and maintain data pipelines for collection, preprocessing, feature extraction, and labeling.
• Prototype algorithms in Python (e.g., PyTorch, TensorFlow) and collaborate with embedded engineers to create deployable, resource-efficient models.
• Evaluate model performance using appropriate metrics; iterate to improve robustness and generalization across platforms and use cases.
• Work with robotics and vehicle engineers to understand requirements and convert them into concrete ML problems and model specifications.
• Support data visualization, dashboards, and tools for internal users to interpret model outputs and system behavior.
• Document models, experiments, datasets, and results to ensure reproducibility and traceability.

• Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
• Hands-on experience with machine learning for time-series or sensor data.
• Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow.

Experience working with real-world noisy data (e.g., automotive, robotics, industrial, IoT).

• Familiarity with data science tools and workflows (NumPy, Pandas, Jupyter, etc.).
• Ability to work in a cross-functional team and communicate technical concepts clearly.

• Experience with embedded / edge AI or model compression and optimization techniques.
• Familiarity with control systems, robotics, or vehicle dynamics.
• Experience with MLOps tools (experiment tracking, model versioning, CI/CD for ML).
• Prior work in a product or R&D environment with multi-disciplinary teams.

Expected Compensation

Annual salary + benefits

Pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.

Applicants for employment at Microvast must be a U.S. citizen or national, U.S. permanent resident (i.e. current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

Microvast is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to any factor, including veteran status and disability status, protected by applicable federal, state or local laws. Microvast is also committed to working with and providing reasonable accommodations to individuals with disabilities. Please let your recruiter know if you need an accommodation at any point during the interview process.

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