In this position...
As a Robotics Research Engineer, you will join Ford's Research & Advanced Engineering organization to help design the future of automotive manufacturing and automation. You will work with peers in the Robotics & Automation Research team – a multidisciplinary group of experts focused on applied research and development – to develop scalable solutions that integrate with diverse sets of hardware and software. You will work with downstream peers in other departments, such as Ford’s Manufacturing Technology Development group, to deliver solutions that meet their needs and align with their strategy.
This role will be responsible for prototyping integration of various learning-based robotic control methods as well as helping to guide research that will define Ford’s long-term strategy for in-house model development. This role will focus on groundbreaking technologies with potential for significant business impact in a 3-to-5 year time horizon, and work with key University Alliances to cultivate technology beyond the 5-year timeframe.
The ideal candidate for this position will possess a depth of knowledge related to developing and applying learning-based algorithms in the robotics and automation space, and a breadth of knowledge related to the relevant domains of perception, prediction, motion planning, and control. The ideal candidate will also possess a strong curiosity for discovering a customer’s needs and a keen eye for problems whose solutions will create value for the company.
Responsibilities
What you'll do...
- State-of-the-Art Robotics Research: Define, plan, and execute projects that will push the boundaries of what is possible in manufacturing automation. Leverage and grow your own expertise in modern learning algorithms to create robotics platforms that handle complex, dexterous manipulation tasks while remaining responsive to the world around them. Make solutions that are robust and intuitive to the end user, and that generalize and adapt with minimal effort.
- Collaboration and Fast Prototyping: Work together with a multidisciplinary team to quickly develop proof-of-concept platforms, contributing your own unique insight and expertise. Communicate with downstream customers to anticipate their needs and align Ford’s research to fill the gaps before they arise. Cooperate with cross-functional teams to leverage Ford’s expansive knowledge base and connect your work to others’ to increase value.
- Technical Excellence & Innovation: Advance Ford’s intellectual property portfolio by writing new patent applications and contribute to the scientific community through publications in top-tier conferences and journals. Work with university partners to nurture advances in key robotics and automation concepts that will give us an advantage beyond a 5-year timeframe.
Qualifications
You’ll have …
- Master's degree in Electrical, Mechanical, Computer Engineering, Robotics, Computer Science, or a related technical discipline and 3+ years of experience developing and implementing learning algorithms for robotic systems or a Ph.D. in Electrical, Mechanical, or Computer Engineering, Robotics, Computer Science.
- 1 year experience of experience applying state-of-the-art learning algorithms, such as foundational models (LLMs, VLMs, VNMs, Vision-Language-Action models), Reinforcement Learning, Transfer Learning, or Imitation Learning.
- 1 year programming experience in C++ and Python
- Strong written and oral communication skills to convey ideas to those with limited theoretical background in the area in a concise manner
Even better, you may have …
- Ph.D. in Electrical, Mechanical, or Computer Engineering, Robotics, Computer Science, or a closely related technical field
- Ability to work independently on deep research tasks as well as collaborate with a multi-disciplinary team to integrate proof-of-concepts
- An agile mindset that strives to deliver results while adapting to evolving priorities and deadlines
- Proven innovation through research publication in leading robotics and AI conferences (e.g., ICRA, IROS, RSS, NeurIPS, CVPR) and granted or pending patents in related fields
- Experience with one or more of the following areas: 3D perception; kinematics and control for 6+ DoF robots; modern motion planning and controls algorithms; multi-agent systems; human-robot interaction
- Experience using simulation environments such as Isaac Sim, Gazebo, PyBullet, or others to speed system development and validation
- Business acumen and comfort assessing the financial viability of proposed projects
