Engineer -AI/ML -Time Series & Robotics

Carter Murray; Houston (TX); On-site; USD 100,000 - 140,000; Full time

Benefits offered by this job

Competitive base salary

Benefits package

Job summary

Carter Murray in Houston seeks an AI/ML Engineer to develop time-series ML models for multi-sensor data from vehicles and robots, focusing on robust deployment and monitoring.

You will prototype in Python with PyTorch/TensorFlow, translate to production in C++, collaborate with robotics and embedded teams, and contribute to data pipelines, dashboards, and reproducible experiments.

Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, Applied Mathematics, or related field.
• Experience with embedded AI, edge AI, or model compression/optimization.
• Hands-on ML for time-series or sensor data.
• Strong C++ and modern ML frameworks knowledge.
• Experience with real-world noisy data in automotive/robotics/IoT.
• Familiarity with NumPy, Pandas, and Jupyter.
• Ability to work in cross-functional teams and explain technical concepts clearly.

Responsibilities

• Design and implement ML models for time-series sensor data.
• Build data pipelines for collection, preprocessing, feature extraction, labeling.
• Prototype Python algorithms using PyTorch/TensorFlow.
• Collaborate with embedded engineers to deploy resource-efficient models.
• Develop production-level C++ models to improve runtime efficiency.
• Evaluate model performance and improve robustness across platforms.
• Support data visualization, dashboards, and internal tools.
• Document models, experiments, datasets, and results.

Skills

C++ proficiency

Python

Time-series ML

ML frameworks

Embedded/edge AI

Education

Bachelor’s/Master’s/PhD in CS/EE/Applied Math

Tools

NumPy

Pandas

Jupyter

PyTorch

TensorFlow

ROS

Job description

AI/ML Engineer – Time Series & Robotics

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.

Compensation & Benefits

• 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.