Robotics Solutions Engineer – Robotics Data Collection & Physical AI
New
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Innodata Inc.Data Engineering, AI
Remote - United StatesFull-TimeMiddle
Salary175000 - 225000 USD per year
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Job Details
- Experience
- 3+ years
- Required Skills
- PythonJavaC++Computer Vision
Requirements
- Strong background in robotics, computer vision, or embodied / Physical AI
- Experience building or training real robotic or simulation-based systems
- Systems-level mindset: able to move from physical task → model behavior → data representation → metrics
- Familiarity with world models, imitation-learning or teleoperation pipelines, simulation-based workflows, or synthetic data generation for robotics
- 3+ years of hands-on development in Python
- 3+ years of hands-on development in C++/Java or similar languages
- Comfort operating in ambiguous, early-stage problem spaces; you can rapidly scope MVP solutions and iterate with customers.
- Clear technical communication and a customer-facing solution-architect mindset
- Strong project-management and ownership skills
- Experience with real robotic platforms (humanoids, manipulators, mobile robots) or advanced simulators and digital-twin platforms.
- Experience designing large-scale datasets, annotation schemes (e.g., affordances, action labels, dense CV annotations), or evaluation pipelines for robotics/Physical-AI models.
- Prior solutions-engineering, pre-sales, or consulting experience with technical customers, especially in frontier robotics or autonomous systems.
- Contributions to open-source robotics/ML projects, technical blogs, or publications that demonstrate hands-on prototyping, dataset design, or applied research.
Responsibilities
- Design Physical AI problem formulations that map real-world robotic behavior into concrete data and evaluation requirements for training policies, world models, and perception systems.
- Prototype perception, world-model, and action-representation pipelines (e.g., VLMs, VLAs, world models) to understand what data is needed, why it matters, and how quality will be measured.
- Use simulation and synthetic environments (digital twins, Isaac/Omniverse-style tools) to generate, stress-test, and scale datasets for robotics and humanoid systems, grounded in real sensors, tasks, and constraints.
- Work directly with customers’ robotics and ML teams to define data specifications, collection strategies (egocentric capture, teacher-follower demonstrations, imitation learning), and evaluation benchmarks that tie to model performance and business outcomes.
- Lead technical discovery and pre-sales pilots: scope projects, design experiments, and secure the “technical win” by demonstrating uplift from our data, annotations, and pipelines.
- Collaborate with internal data-collection and platform teams to design robust data pipelines, annotation workflows (including affordances and advanced CV labels), and QA processes that generalize across customers.
- Develop reusable playbooks, reference architectures, and demos for common Physical-AI use cases (manipulation, mobile navigation, teleoperation, human-robot interaction) to accelerate future engagements.
- Influence the product and tooling roadmap by bringing structured feedback from frontier robotics customers, and help shape a scalable “Robotics & Physical AI data platform.”
- Represent the company at key industry events and workshops, evangelizing best practices for robotics data, simulation, and evaluation and helping build a broader data and partner ecosystem.
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