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.
• Build evaluation frameworks for explanation quality across dimensions such as faithfulness, sufficiency, consistency, and plausibility of explanations.
• Design and build contestability capabilities that enable users to challenge AI outputs, capture corrective feedback, and turn contested results into data that improves systems over time.
• Work on adjacent high-impact areas such as hallucination detection and mitigation, and continual-learning agents that can 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 AI systems, including LLM/VLMs, agent frameworks, RAG, or adjacent applied ML systems.
• Strong Python and software engineering fundamentals, with comfort in testing, code review, CI/CD, debugging, and performance analysis.
• 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.
• Clear communication and strong collaboration across technical and non-technical partners.

Preferred Qualifications, Research Scientist-Leaning Candidates:

• Master’s or PhD in computer science, machine learning, AI, statistics, or a related field preferred.
• Experience in explainable and interpretable AI, such as feature attribution methods like LIME and SHAP, example- or influence-based attribution, or mechanistic interpretability.
• Track record of original technical work, such as publications, patents, open-source contributions, or research that materially shaped shipped systems.

Preferred Qualifications, Engineer-Leaning Candidates:

• Experience designing end-to-end AI systems from data preparation and evaluation through serving, deployment, monitoring, and iteration.
• Experience with inference and serving stacks such as vLLM, SGLang, or similar systems.
• Experience optimizing model serving for latency, throughput, batching, caching, memory efficiency, quantization, and cost/performance tradeoffs.
• Experience with API/SDK development and building usable platform abstractions for other developers.
• Experience with Kubernetes-based deployment, CI/CD, and GitOps workflows such as Argo CD.
• Experience working with GPU/accelerator environments, containerized ML workloads, and performance tuning in production.
• Experience with database and retrieval system design, including relational stores, vector databases, and RAG architectures.
• Experience with experiment tracking, model/data versioning, evaluation pipelines, observability, and diagnosing production issues in AI systems.

Nice to Have:

• Experience designing AI systems with human oversight, review, approval, or override workflows.
• Familiarity with governance, provenance, security, and auditability requirements for enterprise or government AI.
• Experience deploying AI across cloud, on-prem, edge, or air-gapped environments.

Why This Role Matters:

Explainability is becoming infrastructure, not a side feature. As AI moves deeper into operational, regulated, and business-critical workflows, the systems that win will be the ones people can interrogate, validate, and defend. This role is a chance to help define that standard at a company built around trusted AI from the start.