Robotics Machine Learning Engineer (Embodied AI)

Join a research team advancing the state of the art in robotic learning, embodied AI, and foundation models. You will help integrate new robotic platforms and simulation environments into a sophisticated learning framework, collect and prepare demonstrations for model training, and launch fine-tuning and evaluation runs across internal and external VLA models. The work combines cutting-edge AI research with hands-on robotics engineering, giving you the opportunity to develop new capabilities and meaningfully influence the direction and impact of emerging research.

How you will make an impact:

• Integrate new simulation environments and robotic hardware, including robotic arms, with robotic learning frameworks by adapting server/client software to new APIs.
• Develop, fine-tune, and improve sophisticated software implementations supporting embodied AI and robotics research.
• Build and maintain automated testing infrastructure for robotics and machine learning systems.
• Develop machine learning training and evaluation pipelines using Azure Machine Learning.
• Collect, convert, analyze, and refine demonstration and training datasets for robotic learning and AI applications.
• Launch fine-tuning and evaluation runs for internal and external vision-language-action models and other robotic learning models.
• Develop tools that enable rapid deployment and evaluation of novel algorithms in simulation and on physical robotic hardware.
• Integrate robotic control and perception stacks with simulation frameworks.
• Troubleshoot and unit test new and legacy systems to identify and resolve complex software and integration issues.
• Develop new research features, perform evaluations, and create technical documentation supporting research objectives.

1. Python - 5+ years of professional or advanced research experience developing production-quality software, machine learning pipelines, robotics integrations, and tools for training and evaluating AI models.
2. PyTorch - 2+ years of hands-on experience developing, training, fine-tuning, or evaluating machine learning models, ideally including vision-language-action models, diffusion models, reinforcement learning policies, or related foundation models.
3. ROS2 - 1+ year of hands-on experience integrating robotic hardware, control systems, perception stacks, sensors, or simulation environments using ROS2.
4. Robotics & Simulation - Demonstrated experience working with robotic hardware or simulation environments and integrating robotic systems with machine learning frameworks., * Master’s degree in computer science, computer engineering, robotics, machine learning, or a related technical field required. * 5-7 years of related software engineering, machine learning, robotics, or research experience. * 5+ years of Python programming experience. * 2+ years of hands-on PyTorch experience. * 1+ year of ROS2 experience. * Strong foundation in computer science, including data structures, algorithms, and software design. * Experience with robotic hardware, simulation environments, or both. * Experience integrating robotic control and perception systems with software or simulation frameworks. * Background with machine learning frameworks such as PyTorch or TensorFlow. * Experience developing training and evaluation pipelines for machine learning models. * Strong troubleshooting, debugging, unit testing, and problem-resolution skills across complex software systems. * Experience working with robotic learning approaches such as vision-language-action models, diffusion models, reinforcement learning policies, or related techniques is highly desirable.

What makes a candidate highly successful in this role:

A highly successful candidate will combine advanced machine learning expertise with hands-on robotics and strong software engineering fundamentals. PhD-level research, doctoral studies, or substantial research experience within a robotics lab will be particularly valuable, especially for candidates who have trained robotic models such as vision-language-action models, diffusion models, or reinforcement learning policies. Strong candidates will also be comfortable moving between research and implementation-integrating robotic hardware or simulation environments, adapting APIs, preparing training data, launching model fine-tuning and evaluation runs, and troubleshooting complex systems. Success will be measured by tangible impact on research objectives, including new capabilities, meaningful evaluations, effective technical documentation, and consistent progress against collaboratively planned research priorities.

At Team Red Dog, people are at the heart of everything we do. Our commitment to personalized service and our deep experience in matching talented professionals with meaningful roles at some of the world’s most inspiring companies is what sets us apart. We take the time to understand your unique skills, strengths, and passions-because we believe your career should reflect who you are.

Whether you’re looking to grow, pivot, or simply find a place where your work truly matters, we offer opportunities that empower you to make a positive impact. With excellent benefits, a supportive team, and a role where you can thrive while doing what you love, we’re here to help you take the next step with confidence. Join us-and discover what it means to be genuinely valued in your career.

Generous benefits package for qualified employees includes:

• Health insurance (medical, dental, vision, and life)
• Employer-matched 401K plan
• Paid Time Off
• Flexible Paid Holiday Benefit, We offer competitive compensation aligned with U.S. industry standards, and our final offer will reflect the candidate’s location, job-specific skills, experience, and knowledge.

for our client, a Fortune 50 technology leader and AI-driven technology organization advancing state-of-the-art robotic learning and foundation models. This role will develop and integrate embodied AI systems across robotic hardware and simulation environments, build machine learning training and evaluation pipelines, and fine-tune vision-language-action (VLA), diffusion, and reinforcement learning models. You will work hands-on with Python, PyTorch, ROS2, Azure Machine Learning, robotic control and perception stacks, and simulation frameworks to move novel research algorithms from experimentation to real-world robotic systems. This is an opportunity to directly influence emerging robotics research while working with an agile team at the forefront of AI, machine learning, and embodied intelligence.