AI Engineer / Research Scientist (Senior, Staff), Explainable AI

Seekr's Mission: Seekr builds trusted AI for mission-critical decisions. Our platform helps organizations build, govern, and deploy secure, explainable AI rooted in their own data across cloud, on-premises, edge, and air-gapped environments. We care deeply about transparency, auditability, and defensibility because high-stakes AI is only useful when people can understand and trust how it behaves. About the Opportunity: The first wave of AI was about scale. The frontier now is reliable AI: systems that are not only capable, but understandable, testable, and dependable in real decisions. At Seekr, explainability is not a reporting layer added after deployment; it is a core product and research problem spanning attribution and interpretability, observability, and contestability. This role sits directly in that high-impact space, helping turn state-of-the-art ideas into production capabilities customers can trust. We are open to candidates from either research scientist or engineering backgrounds. Success in this role requires strength in one domain, and working proficiency in the other. What You’ll Do: Design and build explainability capabilities that help users understand why a model or agent produced a given output and what training data, retrieved documents, tools, agent interactions, or internal model mechanisms influenced that result. Design and build contestability ca [... source excerpt omitted ...] learn from explainability signals and contested outputs. Translate and synthesize promising ideas from current literature into prototypes, and translate validated prototypes into production-grade features. Contribute across the AI system lifecycle where needed, including model development, inference, deployment, and monitoring. Partner with product, design, and customer-facing teams to make explainability useful in real workflows, not just technically interesting. Use AI coding assistants effectively and reliably as part of a modern engineering workflow while maintaining strong judgment and code quality. What We’re Looking For: Strong background in machine learning and modern [... source excerpt omitted ...] including LLM/VLMs, agent frameworks, RAG, or adjacent applied ML systems. Ability to move comfortably between research and engineering. Scientists here should be able to write production-grade code when needed; engineers here should be able to prototype and pressure-test systems inspired by state-of-the-art papers. Experience designing experiments and evaluating ambiguous technical tradeoffs. Fluency with AI coding assistants and the modern developer workflows they enable. Strong Python and software engineering fundamentals, with comfort in testing, code review, CI/CD, debugging, and performance analysis. Clear communication and strong collaboration across technical and non