Are you a master of the AI lifecycle who can thrive at the intersection of deep learning research, cybersecurity and large-scale production systems? Do you possess the rare ability to jump into a complex technical crisis, diagnose a bottleneck in a Small Language Model (SLM), and lead a team to a production-ready solution?
As a Distinguished AI/ML Architect, you will be the premier technical authority across the Cortex AI/ML organization. You will serve as the "force multiplier" for multiple AI & ML teams, ensuring that our AI strategy – from endpoint-deployed DL/ML models to cloud-scale agentic products – is executed with world-class precision. You will operate as the primary technical architect and hands-on leader for our most ambitious and difficult initiatives.
Key Responsibilities
Operate at the forefront of AI and cybersecurity, leveraging vast datasets to design and deploy innovative defense mechanisms.
Provide horizontal technical leadership across endpoint, cloud, and agentic AI teams to ensure architectural excellence.
Oversee the design and deployment of different model architectures to guarantee scalability and high-performance standards.
Spearhead high-difficulty initiatives and new research frontiers, moving them from ambiguity to production.
Resolve complex technical bottlenecks across the stack, optimizing model efficiency and runtime performance.
Align research and engineering efforts to transform advanced models into robust, production-grade products.
As a Distinguished AI/ML Architect, you will be the premier technical authority across the Cortex AI/ML organization. You will serve as the "force multiplier" for multiple AI & ML teams, ensuring that our AI strategy – from endpoint-deployed DL/ML models to cloud-scale agentic products – is executed with world-class precision. You will operate as the primary technical architect and hands-on leader for our most ambitious and difficult initiatives.
Key Responsibilities
Operate at the forefront of AI and cybersecurity, leveraging vast datasets to design and deploy innovative defense mechanisms.
Provide horizontal technical leadership across endpoint, cloud, and agentic AI teams to ensure architectural excellence.
Oversee the design and deployment of different model architectures to guarantee scalability and high-performance standards.
Spearhead high-difficulty initiatives and new research frontiers, moving them from ambiguity to production.
Resolve complex technical bottlenecks across the stack, optimizing model efficiency and runtime performance.
Align research and engineering efforts to transform advanced models into robust, production-grade products.
Requirements:
Required Qualifications
10+ years of hands-on experience delivering production-grade machine learning and deep learning projects at scale.
Proven ability to own the entire lifecycle of a project-taking ambiguous ideas from initial research through to successful production deployment.
Extensive experience designing, training, and fine-tuning complex models, including SLMs and LLMs, tailored to specific proprietary datasets.
Track record of shipping diverse models to production across both resource-constrained endpoints and high-throughput cloud environments.
Deep expertise in building and optimizing agentic AI systems, including RAG architectures and autonomous workflows.
Demonstrated ability to lead technical strategy, ensuring research code is scalable, reliable, and production-ready.
Advanced degree (MSc or PhD) in Computer Science, Machine Learning, Physics, Mathematics, or a related quantitative field.
Excellent communication skills, with the ability to articulate complex architectural decisions to both technical teams and leadership.
Preferred Qualifications
Background in the cybersecurity domain, specifically in developing AI/ML models to detect and prevent cyber attacks.
Deep cybersecurity expertise in non-AI fields, such as vulnerability research, reverse engineering, or low-level security systems development.
Experience with low-level performance engineering, including model quantization, pruning, and runtime frameworks like ONNX or TensorRT.
Required Qualifications
10+ years of hands-on experience delivering production-grade machine learning and deep learning projects at scale.
Proven ability to own the entire lifecycle of a project-taking ambiguous ideas from initial research through to successful production deployment.
Extensive experience designing, training, and fine-tuning complex models, including SLMs and LLMs, tailored to specific proprietary datasets.
Track record of shipping diverse models to production across both resource-constrained endpoints and high-throughput cloud environments.
Deep expertise in building and optimizing agentic AI systems, including RAG architectures and autonomous workflows.
Demonstrated ability to lead technical strategy, ensuring research code is scalable, reliable, and production-ready.
Advanced degree (MSc or PhD) in Computer Science, Machine Learning, Physics, Mathematics, or a related quantitative field.
Excellent communication skills, with the ability to articulate complex architectural decisions to both technical teams and leadership.
Preferred Qualifications
Background in the cybersecurity domain, specifically in developing AI/ML models to detect and prevent cyber attacks.
Deep cybersecurity expertise in non-AI fields, such as vulnerability research, reverse engineering, or low-level security systems development.
Experience with low-level performance engineering, including model quantization, pruning, and runtime frameworks like ONNX or TensorRT.
This position is open to all candidates.
