Trusted AI for critical infrastructure to deliver secure, explainable agents across cloud, on-prem, and edge with rapid time to impact.
About the role
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.
What they're looking for
- Strong background in machine learning and modern AI systems, 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
More about this role
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.
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.
- Design and build explainability capabilities that help users understand why a model or agent produced a given output and what training...
Browse similar: AI jobs · AI startup jobs · Startup jobs