Staff/Principal Machine Learning Engineer, Okta

• 5+ years of hands-on experience developing and deploying impactful AI/ML solutions (including deep learning) using frameworks like TensorFlow, PyTorch, or Scikit-learn
• Experience with prompt engineering and leveraging LLM ecosystems (e.g., Hugging Face, LangChain, vector databases)
• Proven track record of designing, training, validating, deploying, and monitoring ML models that achieve measurable business outcomes
• Strong practical experience with cloud AI/ML services (AWS SageMaker and AWS Bedrock preferred)
• Proficiency in debugging, performance tuning, and optimizing large-scale data processing and ML models
• Demonstrated ability to collaborate effectively with product owners, tech leads, and cross-functional teams; experience mentoring engineers, building trust with senior leaders, and acting as a strategic advisor

Desirable

• Okta seeks a visionary Staff/Principal Machine Learning Engineer to spearhead AI/ML integration into our identity management solutions
• As the key AI/ML architect and strategist for our Identity Management pillar, you'll shape our product future (notably Okta Identity Governance), identify opportunities, design innovative solutions using cutting-edge techniques like LLMs, and guide engineering teams
• You will solve complex identity challenges, deliver significant customer value, and define the AI strategy for one of Okta's fastest-growing product areas
• Lead and Architect: Drive the end-to-end design, development, and deployment of AI/ML-driven features within the Identity Management platform, ensuring measurable business outcomes and alignment with product strategy
• Strategic Guidance: Serve as the primary technical advisor on AI/ML matters for the Identity Management pillar, evaluating new technologies (including LLMs, vector databases, ML frameworks) and defining best practices
• Technical Leadership & Mentorship: Engage in deep technical design discussions, conduct code reviews, and mentor engineers across the pillar, uplifting the team's capabilities in applying AI/ML to identity problems
• Cross-functional Collaboration: Work closely with Product Management, architects, and other engineering teams to integrate AI/ML capabilities seamlessly into the product roadmap and user experience
• Optimization & Refinement: Apply optimization techniques to enhance model performance and accuracy; utilize enterprise data to iteratively refine AI models specific to identity use cases