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Software Engineer - AI Sysml Jobs

3 Job Offers

Software Engineer - AI SysML
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United Kingdom , London
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Not provided
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Meta
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Until further notice
Software Engineer - AI SysML
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Lead the development of next-generation GenAI data infrastructure at Meta. Design and scale high-throughput, low-latency storage and dataloading systems for large language models. Drive technical direction and collaborate cross-functionally in Bellevue. Requires expertise in AI/ML data systems an...
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United States , Bellevue
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217000.00 USD / Year
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Meta
Expiration Date
Until further notice
Software Engineer - AI SysML (Technical Leadership)
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Lead AI infrastructure projects at Meta as a Software Engineer in AI SysML. Apply your deep learning and ML systems expertise to solve critical web-scale challenges. This technical leadership role offers bonus, equity, and benefits in Sunnyvale. Drive industry-wide impact with a cross-functional ...
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United States , Sunnyvale
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219000.00 - 301000.00 USD / Year
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Meta
Expiration Date
Until further notice

About the Software Engineer - AI Sysml role

Software Engineer - AI Sysml Jobs represent a specialized intersection of artificial intelligence, systems engineering, and model-based design. Professionals in this field focus on developing and maintaining complex systems using SysML (Systems Modeling Language) and AI-driven methodologies to enhance system architecture, requirements management, and verification processes. This role is critical in industries where precision, safety, and scalability are paramount, such as aerospace, automotive, defense, and industrial automation.

The primary responsibilities of a Software Engineer in AI Sysml include creating and refining system models that capture functional, behavioral, and structural aspects of complex products. They use SysML to establish traceability between high-level requirements and detailed design elements, ensuring that every component aligns with overall system goals. A key aspect of the job involves integrating AI algorithms into these models to automate analysis, optimize performance, and predict system behavior under various scenarios. This might include developing machine learning models for anomaly detection, using AI to assist in trade-off studies, or employing natural language processing to extract requirements from unstructured documents. Additionally, these engineers often work with simulation tools to validate models, perform safety assessments like Failure Mode and Effects Analysis (FMEA), and generate documentation for certification or compliance purposes. Collaboration is central to the role, as they interface with hardware engineers, software developers, and project stakeholders to ensure coherent system integration.

Typical skills and requirements for Software Engineer - AI Sysml jobs include a strong foundation in systems engineering principles and proficiency in SysML, often through tools like Cameo Systems Modeler or IBM Rhapsody. Expertise in AI and machine learning frameworks (e.g., TensorFlow, PyTorch) is increasingly essential, along with knowledge of data modeling and algorithm development. Programming skills in languages such as Python, C++, or Java are common, as is experience with model-based systems engineering (MBSE) workflows. A background in embedded systems, real-time software, or safety-critical applications is highly valued, particularly for roles in regulated industries. Familiarity with standards like DO-178C, ISO 26262, or MIL-STD-1553 is often required. Soft skills such as analytical thinking, problem-solving, and effective communication are critical, as these engineers must translate complex technical concepts across multidisciplinary teams. Educational requirements typically include a bachelor’s or master’s degree in computer science, electrical engineering, systems engineering, or a related field, with many positions seeking candidates who hold certifications in MBSE or AI specialties.

In summary, Software Engineer - AI Sysml jobs offer a dynamic career where technology meets rigorous engineering discipline. Professionals in this field drive innovation by blending AI capabilities with structured systems modeling, enabling the creation of smarter, safer, and more efficient systems. As industries increasingly adopt digital engineering practices, the demand for these hybrid roles continues to grow, making it a compelling path for those passionate about both artificial intelligence and complex system design.