About the Role :

We are building the infrastructure that powers the next generation of Physical AI from multimodal data collection and AI training pipelines to model evaluation, deployment, and simulation workflows.

As a Senior AI Systems Engineer, you will work at the intersection of AI, distributed systems, and robotics. You will design and build the platforms that enable AI models to learn from real-world interactions, process multimodal data at scale, and bridge the gap between simulation and physical deployment.

This role is ideal for engineers who enjoy solving large-scale engineering problems and are excited about applying AI to real-world robotic systems.

What You'll Do :

- Design and build scalable AI infrastructure for multimodal data collection, processing, and storage.

- Develop data pipelines that support training, evaluation, and deployment of AI models.

- Build tooling for data validation, visualization, replay, and debugging.

- Design systems for managing large-scale multimodal datasets.

- Develop infrastructure for offline evaluation, benchmarking, and continuous model improvement.

- Collaborate with AI researchers and robotics engineers to operationalize new models and workflows.

- Support deployment of multimodal AI models into production environments.

- Design systems that enable simulation-to-reality (Sim2Real) and reality-to-simulation (Real2Sim) workflows.

- Build reliable, observable, and scalable backend services that power Physical AI platforms.

Required Qualifications :

- 4+ years of professional software engineering experience.

- Strong Python programming skills.

- Experience building and deploying AI or machine learning systems in production.

- Experience designing scalable backend systems and distributed architectures.

- Experience building data-intensive platforms or large-scale data pipelines.

- Strong understanding of software architecture, system design, and engineering best practices.

- Experience working with multimodal data such as images, video, sensor data, text, or time-series data.

- Excellent debugging, problem-solving, and communication skills.

Preferred Qualifications :

- Experience with computer vision, multimodal AI, or perception systems.

- Experience with Vision-Language Models (VLMs), Vision-Language-Action (VLAs), or other foundation models.

- Experience deploying AI inference pipelines at scale.

- Experience designing data validation, dataset management, or offline evaluation systems.

- Experience with simulation platforms such as NVIDIA Isaac Sim, Gazebo, MuJoCo, or Omniverse.

- Experience with Sim2Real or Real2Sim workflows.

- Experience with robotics software such as ROS/ROS2, rosbag, or MCAP.

- Experience working with point clouds, depth cameras, LiDAR, or other robotics sensors.

- Experience with multi-sensor synchronization, calibration, or timestamp alignment.

- Strong C++ experience for high-performance systems.

Nice to Have :

- Experience with Physical AI or embodied AI.

- Experience building robotics or autonomous systems.

- Experience with teleoperation or robotics data collection.

- Experience building developer platforms or internal engineering tools.

- Experience with large-scale data infrastructure and distributed processing systems.

- Experience in startup or fast-paced product environments.

What Success Looks Like :

In this role, you'll help build the engineering foundation that enables Physical AI systems to learn, improve, and operate reliably in the real world. You'll work across AI, infrastructure, and robotics to deliver scalable platforms that support data collection, model development, evaluation, and deployment.

If you're passionate about building production-grade AI systems and excited by the challenges of bringing intelligence into the physical world, we'd love to hear from you.