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

Seekr • Austin, TX

2d • Hybrid

About The Position

Seekr is seeking an AI Engineer or Research Scientist with expertise in Explainable AI (XAI) to join their team. This role focuses on developing reliable and understandable AI systems for mission-critical decisions. The position involves designing and building capabilities for users to understand AI outputs, evaluate explanation quality, and challenge AI decisions. Responsibilities also include working on areas like hallucination detection, continual learning, and translating research into production-grade features. The role requires a strong background in machine learning, modern AI systems (LLMs, agents, RAG), and Python software engineering. Candidates should be comfortable moving between research and engineering, designing experiments, and communicating effectively. The company emphasizes that explainability is a core product and research problem at Seekr, crucial for transparency, auditability, and defensibility in high-stakes AI applications.

Requirements

• 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.

Nice To Haves

• 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.
• 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.
• 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.

Responsibilities

• 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.

Benefits

• Meaningful Mission & Impact - Work with a deeply talented, collaborative team solving some of the toughest AI challenges that matter.
• Equity Ownership – RSUs that let you share directly in Seekr’s long‑term success and growth.
• Time Off That Respects Real Life – Unlimited PTO plus 14 paid company holidays to truly recharge.
• Work Your Way – A flexible hybrid work environment with offices in Reston, VA and Austin, TX, plus remote options and flexible working hours.
• Competitive Total Rewards – A role‑appropriate compensation structure that supports long‑term growth, including base salary, bonuses, or commission plans depending on role.
• 401(k) with Company Match – Build your future with a retirement plan that includes employer matching.
• Comprehensive Health & Wellness – Medical, dental, vision, and life insurance coverage starting day one—for you and your family.
• Parental Leave – Paid parental leave to support employees as they welcome a new child through birth, adoption, or foster placement.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior