Staff AI / Machine Learning Engineer, Blackbird AI

• This is a hands-on technical leadership role for engineers who enjoy shipping complex AI systems—not just experimenting with models
• 7+ years of experience in software engineering, ML engineering, or applied AI or equivalent senior experience with demonstrated Staff-level impact
• Strong experience building production AI systems, not just prototypes
• Deep proficiency in Python and modern ML/AI frameworks
• Hands-on experience with LLMs, including prompting, retrieval-augmented generation, tool use, and evaluation
• Experience designing explainable and auditable ML systems
• Solid understanding of API-driven architectures and data contracts
• Experience working with graph-structured or networked data, including reasoning over entities and relationships
• Ability to reason about performance, cost, and reliability in real systems
• Strong written and verbal communication skills
• Pragmatic AI: You know how to turn cutting-edge ideas into dependable systems
• Explainability: You care deeply about transparency and trust
• Ownership: You take responsibility for outcomes, not just code
• Collaboration: You work effectively across engineering and product boundaries
• Impact: You focus on solving meaningful, real-world problems
• Continuous Learning: You evolve with the AI landscape without chasing hype
• This role is scoped at the Staff level. Candidates with exceptional experience and demonstrated organization-level impact may be considered for Senior Staff or Principal level

Desirable

• As a Staff AI / Machine Learning Engineer, you will design and build production-grade AI systems that combine large language models, machine learning, and structured data
• You will focus on turning advanced AI capabilities into reliable, explainable, and scalable platform features, working closely with data and backend engineers to ensure real-world performance and trustworthiness
• Build and operate production-grade AI systems that combine machine learning, large language models, and structured data
• Translate ambiguous problem spaces into reliable, explainable AI capabilities that customers can trust
• Apply machine learning and AI techniques to graph-structured and networked data, enabling reasoning over entities, relationships, and complex information structures
• Design AI systems with strong guarantees around correctness, transparency, and performance
• Partner with data and backend engineers to bring AI capabilities into scalable platform services
• Evaluate, iterate, and improve AI systems using both quantitative metrics and real-world feedback
• Make thoughtful trade-offs across model quality, latency, cost, and system complexity
• Own AI components end-to-end, from initial design through deployment and long-term maintenance
• Provide technical leadership through design reviews, mentorship, and shared best practices