Software Engineer

We are seeking a skilled and motivated Senior MLOps Engineer to join our engineering team. In this role, you will design and maintain the infrastructure and tooling that supports the full lifecycle of machine learning systems used in robotics applications. You will work closely with machine learning engineers, robotics engineers, and infrastructure teams to ensure reliable training, evaluation, deployment, and monitoring of ML models. This is an exciting opportunity to help operationalize machine learning in real-world robotic systems within a fast-growing and dynamic environment. What You Will Get To Do

• Design, build, and maintain GPU based infrastructure for machine learning pipelines, including data processing, training, evaluation, inference and deployment workflows.
• Collaborate closely with robotics teams to implement model serving infrastructure for edge/robot deployment.
• Build tools and automation to support reproducible experiments, model versioning, and dataset management.
• Deploy and manage ML services and inference pipelines using containerized environments for efficient scaling and scheduling of heterogeneous compute resources.
• Monitor model performance and system reliability across development and production environments.
• Improve the efficiency, scalability, and reliability of ML workflows and infrastructure.
• Work with cross-functional engineering teams to integrate ML components into robotics software systems., Our salary range is competitive with the market, but we take into consideration an individual’s background and experience in determining final salary; base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. Also, while we enjoy being together on-site, we are open to exploring a hybrid or remote option. Why Join Field AI? We are solving one of the world’s most complex challenges: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models set a new standard in perception, planning, localization, and manipulation, ensuring our approach is explainable and safe for deployment. You will have the opportunity to work with a world-class team that thrives on creativity, resilience, and bold thinking. With a decade-long track record of deploying solutions in the field, winning DARPA challenge segments, and bringing expertise from organizations like DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise Self-Driving, Zoox, Toyota Research Institute, and SpaceX, we are set to achieve our ambitious goals. Be Part of the Next Robotics Revolution To tackle such ambitious challenges, we need a team as unique as our vision - innovators who go beyond conventional methods and are eager to tackle tough, uncharted questions. We’re seeking individuals who challenge the status quo, dive into uncharted territory, and bring interdisciplinary expertise. Our team requires not only top AI talent but also exceptional software developers, engineers, product designers, field deployment experts, and communicators. We are headquartered in always-sunny Irvine, Southern California and have US based and global teammates. Join us, shape the future, and be part of a fun, close-knit team on an exciting journey! We celebrate diversity and are committed to creating an inclusive environment for all employees. Candidates and employees are always evaluated based on merit, qualifications, and performance. We will never discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability, or any other legally protected status. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

• Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent work experience).
• 3-7 years of experience in MLOps, machine learning infrastructure, or related engineering roles.
• Strong programming skills in Python or similar languages.
• Experience building and maintaining machine learning pipelines.
• Hands-on experience with cloud and cloud-native tools such as AWS (SageMaker, S3, or similar cloud ML services), Kubernetes etc.,
• Solid understanding of Linux systems and distributed computing environments.
• Experience with GPU workload scheduling and orchestration across multi-region cloud environments.
• Excellent problem-solving skills and the ability to work collaboratively in a team environment.

What Will Set You Apart

• Experience deploying and operating ML systems for robotics or real-world physical systems.
• Experience with scaling AI, ML, and inference workloads on Kubernetes.
• Exposure to ROS-based robotics data formats and pipelines (rosbags, point clouds)
• Experience with experiment tracking, model versioning, or dataset versioning tools.
• Experience optimizing ML pipelines for large-scale training and data processing.
• Experience working closely with research or applied machine learning teams., Amazon Simple Storage Service (S3), Amazon Web Services (AWS), Artificial Intelligence (AI), Automation, Cloud Computing, Compensation and Benefits, Computer Programming, Computer Science, Computer Systems, Cross-Functional, Data Processing, Distributed Computing, Diversity, Equipment Maintenance/Repair, GPU (Graphics Processing Unit), Linux Operating System, Localization, Machine Learning, Machine Tool, Performance Analysis, Performance Modeling, Problem Solving Skills, Product Design, Production Systems, Python Programming/Scripting Language, Risk, Robotics, Robotics Components, Robotics Software, Software Development, Software Engineering, System Lifecycle, Systems Reliability, Team Player, Training Data Sets