Robotics Systems & Simulation Engineer

Xaira is an innovative biotech startup focused on leveraging AI to transform drug discovery and development. The company is leading the development of generative AI models to design protein and antibody therapeutics, enabling the creation of medicines against historically hard-to-drug molecular targets. It is also developing foundation models for biology and disease to enable better target elucidation and patient stratification. Collectively, these technologies aim to continually enable the identification of novel therapies and to improve success in drug development. Xaira is headquartered in the San Francisco Bay Area, Seattle, and London.

We are building a next-generation lab automation and robotics platform that brings together robotic systems, simulation, computer vision, and AI to improve the speed, reliability, reproducibility, and scale of experimental biology.

As a Robotics Systems & Simulation Engineer, you will connect digital environments with physical systems and make robotic capabilities easier to adapt to new equipment and settings. You will own the simulation and digital-twin infrastructure that lets us bring vision-guided robots to new instruments quickly, and build the venue to test robotics control, perception and embodied AI. Working alongside robotics, AI, software, and automation engineers, you will develop and validate integrated systems from prototype through real-world operation.

This is a on-site hands-on role spanning software, 3D geometry, simulation, and hardware. We value strong fundamentals, practical problem-solving, and the ability to learn across disciplines using modern engineering and AI development tools.

• →Build integrated Real2Sim and Sim2Real workflows that can be reused across robotic platforms and laboratory environments.
• →Create and validate digital twins from CAD, robot and sensor models, and reconstructed scenes, including coordinate alignment, articulation, and collision geometry.
• →Connect simulation, perception, motion planning, and physical execution through the existing robot control stack, including robot state feedback and synchronization; investigate discrepancies between simulated and measured behavior.
• →Integrate AI models and task-level state machines with simulation and robotic tools, including structured requests, action validation, and execution feedback.
• →Build synthetic-data and augmentation workflows, along with synchronized video and telemetry capture, replay, and evaluation tools.
• →Benchmark system accuracy, reliability, latency, and GPU resource use; improve performance across local and cloud computing environments.
• →Collaborate with software engineers on reusable configuration and operator tools; validate new installations and reduce manual setup effort.
• →Work with scientists to integrate new capabilities, debug physical systems, and document architecture, experiments, and operating procedures.

• Bachelor's, Master's, or PhD in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or a related field. Recent graduates with substantial relevant research or project experience are encouraged to apply.
• Strong software engineering skills in one or more languages such as Python, C++, or C#, with experience developing and debugging integrated applications in Linux.
• Hands-on experience building or extending robotics simulations or digital twins using Isaac Sim, MuJoCo, Gazebo, or comparable tools.
• Solid understanding of 3D coordinate transformations, robot kinematics, calibration, and the relationship between simulated models and physical systems.
• Experience integrating robots, sensors, or other physical systems through APIs, SDKs, ROS2, or comparable middleware.
• Familiarity with GPU computing and cloud-based workflows for simulation, AI, or data processing.
• Strong debugging and experimental skills, clear technical communication, and the ability to learn unfamiliar methods while critically validating software and AI-generated outputs.

• Experience with 3D reconstruction, CAD/robot-model pipelines, RGB-D perception, multi-view fusion, or learned scene representations.
• Experience integrating LLMs, vision-language models, AI agents, or Model Context Protocol (MCP) servers with external tools and physical systems.
• Experience with spatial or object reasoning, active perception, or perception of transparent, reflective, or partially occluded objects.
• Experience with video understanding, human-object interaction, learning from demonstrations, imitation learning, or other robot-learning methods.
• Experience with GPU profiling, CUDA programming, accelerated inference, or edge deployment.
• Experience with 6-axis robotic arms, mobile robots, laboratory instruments, or hands-on mechanical prototyping.
• Experience integrating robots, sensors, or other physical systems through APIs, SDKs, ROS2, or comparable middleware.
• Experience mentoring students, interns, or junior engineers.