Senior Robotics Data Engineer/Data Scientist

GM•Warren, MI

19h•Hybrid

About The Position

This role is categorized as hybrid. This means the successful candidate is expected to report to Warren Global Technical Center, or Austin Technical Center three times per week, at minimum [or other frequency dictated by the business if more than 3 days]. The Role We are seeking a Senior Full Stack Robotics Data Engineer / Data Scientist to join our manufacturing data organization. In this role, you will help build the data and data science backbone that enables scalable robotics intelligence across manufacturing environments. You will work across data engineering, applied data science, and production software to turn raw robotics data into trusted systems that support analytics, model development, operational insights, and continuous improvement. This is a senior individual contributor role for someone who is comfortable working across data pipelines, cloud and application development, analytics, and cross-functional problem solving. You will partner with robotics engineers, manufacturing stakeholders, software developers, and other data engineers and data scientists to deliver robust end-to-end solutions that are reliable in production and valuable to the business.

Requirements

Bachelor's degree in Computer Science, Data Science, Robotics, Computer Engineering, Electrical Engineering, Mechanical Engineering, or a related technical field.
5+ years of professional experience in data engineering, software engineering, machine learning engineering, applied data science, or a closely related role.
Strong programming skills in Python and at least one additional language such as Java, C++, or Scala.
Experience building and supporting production-grade data pipelines, backend services, APIs, and full stack data applications at scale.
Hands-on experience with big data, streaming, and distributed systems technologies such as Hadoop, Spark, Kafka, Spark Streaming, Storm, PostgreSQL, Cassandra, or similar platforms.
Experience with cloud platforms, data orchestration frameworks, and data modeling for analytics, business intelligence, and machine learning use cases.
Experience applying data science or machine learning techniques to real-world engineering or operational problems.
Strong software engineering fundamentals, including version control, testing, CI/CD, documentation, automation, and production support.
Strong problem-solving, organizational, communication, and cross-functional collaboration skills in a dynamic environment.

Nice To Haves

Advanced degree in Data Science, Robotics, Computer Science, Computer Engineering, or a related field.
Experience working with robotics, autonomous systems, industrial IoT, manufacturing analytics, or other complex physical systems.
Hands-on experience with ROS/ROS 2, robotics telemetry, logging, replay, or simulation data workflows.
Experience with machine learning lifecycle and data quality tooling, including feature engineering, experiment tracking, model registry, deployment automation, and active learning workflows.
Familiarity with data governance, lineage, auditability, and reproducibility requirements in regulated or safety-adjacent environments.
Experience mentoring engineers, setting technical direction, or establishing engineering standards on complex initiatives.

Responsibilities

Design, build, and maintain end-to-end data and data science solutions for robotics data ingestion, processing, storage, analysis, visualization, and application access.
Create and optimize scalable data pipeline architecture for multimodal robotics and manufacturing data, including sensor streams, telemetry, images, time series, metadata, and operational data.
Partner with robotics, manufacturing, business, and data science stakeholders to define requirements, resolve data issues, and support evolving infrastructure needs.
Build high-quality analytic datasets and delivery mechanisms that support business intelligence, analytics, model development, production monitoring, and decision support.
Apply statistical analysis, machine learning, and data science methods to identify patterns, diagnose issues, and improve system performance.
Translate real-world robotics behavior, anomalies, and failure modes into curated datasets, actionable insights, and measurable improvement loops.
Enable reproducible analytics and model development through data versioning, lineage, traceability, experiment tracking, and production-grade MLOps workflows.
Build and maintain internal tools, APIs, master data, metadata, logical data models, and data standards that improve usability, governance, scalability, and reliability.
Drive process and infrastructure improvements through automation, optimized data delivery, stronger engineering standards, and sound technical architecture.