Robotics Backend & ML Integration Engineer, Site Services

The engineer is responsible for the design, development, testing, deployment, and sustaining of scalable backend systems and machine learning integrations throughout the product life cycle to enable physical automation within Meta data centers. This role collaborates closely with software, robotics, research engineers, and external vendors to ensure seamless system integration, optimizing operational performance, and mitigating latency risks across Meta backend services and external vendor systems.

The ideal candidate has a demonstrated experience in backend software engineering with a focus on machine learning systems integration. They demonstrate expertise in programming languages such as Python and web languages/stacks to serve inference, as well as image processing and time series data. They are skilled in prototyping, version control, hyperparameter tuning, and combining both classical and data-driven approaches to solve complex problems. Additionally, they have a deep understanding of machine learning integration with robot backend systems including model serving, inference optimization, feature stores, and pipeline orchestration. They have experience designing and implementing backend software architectures tailored for fault tolerant machine learning systems, coupled with a deep understanding of model-infrastructure integration. They are proactive problem solvers who can navigate ambiguous requirements, adept at exploring and selecting both open-source and paid software solutions. They thrive in dynamic environments and are passionate about leveraging emerging AI technologies to drive backend innovation. Their technical acumen is complemented by experience communicating technical decisions to technical and non-technical stakeholders and collaborating across teams, enabling seamless teamwork and efficient project execution.

This candidate must demonstrate experience communicating with cross-functional teams, leading projects, managing stakeholder relationships, and applying engineering and analytical methods to solve complex problems. This role requires an experienced, dedicated professional to effectively collaborate and influence internal stakeholders, including cross-functional teams and individuals of all levels.

If you have a interest in emerging technologies and enjoy working in small, agile, empowered teams solving complex problems within a fast-paced, evolving environment then this is the role for you.

Responsibilities

Build and maintain high-throughput pipelines for image, video, and time-series data to support robotics applications Develop core backend services, triage issues, execute bug fixes, and implement long-term design improvements Benchmark classical, modern AI, and hybrid techniques to integrate optimal solutions into production services Fine-tune foundation models on proprietary datasets, benchmark performance, and manage deployment workflows for inference Communicate technical trade-offs and drive backend architecture decisions with cross-functional teams Support and manage scalable simulation stacks and environments for robotic testing and synthetic data generation Integrate internal and external systems via APIs, agent-based workflows, standardized AI context protocols, and secure authentication layers Build bridge layers connecting backends to web components with robust failure-mode handling and fallbacks

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Bachelor’s degree in Computer Science, Software Engineering, Robotics, Electrical Engineering, or a related technical field 5+ years of experience in C++ and Python, focusing on the design, development, and integration of backend services and ML inference pipelines Demonstrated experience developing high-throughput backend data pipelines for image, video, and time-series data streams Strong knowledge of fault-tolerant backend architectures, model serving platforms, inference optimization, and MLOps orchestration tools Hands-on experience with web frameworks, API design, security protocols, and integration layers used to serve ML inference Proven ability to evaluate, benchmark, and integrate open-source and third-party AI, classical, or hybrid software solutions into production Experience navigating ambiguous technical requirements, resolving complex system issues, and executing long-term architectural improvements Strong collaboration skills with cross-functional engineering teams, researchers, and external vendors to deliver production-ready software Experience managing simulation stacks, hardware/software bridge layers, and continuous integration workflows for complex technical systems Track record of mentoring engineers, driving technical best practices, and improving engineering tooling and process Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Master’s degree or above in Computer Science, Machine Learning, Robotics, Electrical Engineering, or a related field Expertise in one or more of the following areas: Familiarity with hardware acceleration and model-infrastructure integration processes Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Track record of contributions to backend software or ML infrastructure communities Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience with data analysis tools and collaboration with research and data science teams Demonstrated ability to scale backend solutions, with a focus on fault tolerance and reliability Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies