Robotics & Physical AI Solutions Architect

Overview

Location:

Remote (US)

Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are the AI technology solutions provider-of-choice to 4 out of 5 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine.

By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of clean and optimized digital data to all industries. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms.

Our global workforce includes over 3,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany. We’re poised for a period of explosive growth over the next few years.

About the Role (Combined Technical + Customer-Facing Role)

We are building a Robotics & Physical AI team focused on systems that perceive, reason, and act in the physical world. Our mission is to design, collect, and evaluate the data that powers frontier robotics and humanoid foundation models. We are looking for a solutions architect who can translate real‑world robotic and embodied‑AI problems into high‑value datasets, data pipelines, and evaluation strategies—while also acting as a trusted technical partner for our customers’ model and product teams.

• 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.

• Strong background in robotics, computer vision, or embodied / Physical AI, with experience building or training real robotic or simulation‑based systems.
• Systems‑level mindset: able to move from physical task → model behavior → data representation → metrics, and explain trade‑offs clearly to both engineers and product leaders.
• 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 and at least one of C++/Java or similar languages used in robotics or ML engineering.
• 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…