About Xaira Therapeutics

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.

About the Role

We are building a next-generation lab automation and robotics platform to transform how biological experiments are executed, monitored, and scaled. This platform will bring together robotic arms, mobile platforms, lab instruments, sensors, computer vision, workflow orchestration, and AI-enabled autonomy to increase the speed, reliability, reproducibility, and scale of experimental biology.

As a Robotics Control Engineer you will help turn real scientific workflows into robust automated systems. You will work closely with biologists, chemists, automation engineers, software engineers, and robotics experts to understand manual laboratory processes and translate them into reliable robotic execution.

This is a hands-on engineering role for someone who enjoys building, integrating, testing, and debugging physical systems. You will contribute to robotic manipulation, instrument integration, perception, task execution, simulation, and multi-station lab automation workflows. You will also have opportunities to grow into more advanced robotics, autonomy, and AI-enabled laboratory automation over time.

What You’ll Do

Build, integrate, and maintain robotics software for robotic arms, mobile robots, sensors, grippers, and laboratory automation devices.
Develop motion planning, manipulation, perception, and safety logic for robotic systems operating in real laboratory environments.
Integrate laboratory instruments and devices through APIs, IoT interfaces, schedulers, and custom control software.
Help build task execution frameworks using state machines, behavior trees, planners, and recovery logic for autonomous or semi-autonomous lab workflows.
Benchmark and integrate computer vision methods for object detection, segmentation, tracking, pose estimation, localization, and scene understanding.
Create and use simulation or digital twin environments for motion validation, workflow testing, synthetic data generation, and system debugging.
Collaborate with scientists to translate biological and chemical workflows into automated protocols.
Support multi-station system integration, automated material handling, sample tracking, validation, logging, and error recovery.
Work hands-on with physical hardware to debug real systems, improve reliability, and iterate quickly.
Evaluate modern AI, autonomy, foundation-model, reinforcement-learning, or imitation-learning approaches where they can improve real robotic lab execution.

Qualifications (Required)

Bachelor’s or Master’s degree in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or related field.
Recent graduates with strong robotics, automation, hardware/software integration, or relevant project experience are encouraged to apply.
PhD candidates are welcome to apply, particularly if they are excited by hands-on robotic system building, integration, and lab automation.
Strong software engineering skills in one or more modern programming languages (e.g., Python, C++, C#), cloud-based workflows, and production software systems.
Familiarity with robotics software frameworks such as ROS2, MoveIt, or comparable tools.
Familiarity with computer vision, perception, motion planning, robotic manipulation, or sensor integration.
Comfort working with physical hardware, debugging real systems, and learning by doing.
Strong problem-solving skills, attention to detail, and ability to work across disciplines.