Engineer -AI/ML -Time Series & Robotics

Carter Murray; Houston (TX); On-site; USD 120,000 - 160,000; Full time

Job summary

Carter Murray is seeking an AI/ML Engineer to develop and deploy machine learning algorithms that analyze multi-sensor time-series data from vehicles and robotic platforms. This role focuses on building robust models that improve system performance, monitoring, and intelligent behavior, working closely with robotics, controls, and embedded systems teams.

You will prototype in Python using PyTorch and TensorFlow, build data pipelines for collection and labeling, and translate prototypes into

Qualifications

• Experience with embedded AI, edge AI, or model compression
• Hands-on ML for time-series or sensor data
• Strong C++ and modern ML frameworks
• Experience with real-world noisy data (auto/robotics/IoT)
• Familiarity with NumPy, Pandas, and Jupyter
• Ability to work in cross-functional teams and communicate clearly

Responsibilities

• Design and implement ML models for time-series sensor data
• Build data pipelines for collection, preprocessing, labeling
• Prototype algorithms in Python using PyTorch and TensorFlow
• Develop production-level models in C++ to improve runtime efficiency
• Evaluate model performance and improve robustness across platforms
• Document models, experiments, datasets, and results

Skills

Time-series ML

Data pipelines

Python ML frameworks

Cross-functional teamwork

Education

Bachelor's degree or higher in CS/EE/Applied Math

Tools

C++

PyTorch

TensorFlow

NumPy

Pandas

Jupyter

Job description

Role Summary

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 systems teams.

Key Responsibilities

• 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 using frameworks such as PyTorch and TensorFlow, and collaborate with embedded engineers to create deployable, resource-efficient models.
• Develop production-level models in C++ to improve runtime efficiency and optimize resource utilization based on Python prototypes.
• Evaluate model performance using appropriate metrics and continuously improve robustness and generalization across platforms and applications.
• Work with robotics and vehicle engineering teams to translate business and technical requirements into machine learning solutions.
• Support data visualization, dashboards, and internal tools used to interpret model outputs and system behavior.
• Document models, experiments, datasets, and results to ensure reproducibility and traceability.

Required Qualifications

• Bachelor's, Master's, or PhD in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
• Experience with embedded AI, edge AI, or model compression and optimization techniques.
• Hands-on experience developing machine learning solutions for time-series or sensor data.
• Strong proficiency in C++ and modern machine learning frameworks.
• Experience working with real-world noisy data in environments such as automotive, robotics, industrial systems, or IoT.
• Familiarity with data science tools and workflows including NumPy, Pandas, and Jupyter.
• Ability to work effectively within cross-functional teams and communicate technical concepts clearly.

Preferred Qualifications

• Familiarity with control systems, robotics, or vehicle dynamics.
• Experience with MLOps tools, including experiment tracking, model versioning, and CI/CD pipelines for machine learning.
• Experience with ROS or other multimodal sensor data frameworks.
• Prior experience in a product development or R&D environment working with multidisciplinary teams.
• Competitive base salary plus benefits.
• Compensation will be determined based on factors such as market conditions, location, experience, skills, and job-related knowledge.
• Total compensation may include additional incentive or benefit programs, depending on the position.