Job description

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 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. 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-technical partners. Reside near Austin, TX or Reston, VA and able to work 3 days per week in office 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 production performance tuning; experience with custom GPU/accelerator kernels for AI workload optimization is a plus. 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. About the Company: Seekr is a leader in explainable and trustworthy artificial intelligence designed to power mission-critical decisions in enterprises, government, and regulated industries. SeekrFlow™, our end-to-end AI platform, provides secure, auditable AI solutions tailored to sectors where transparency, accuracy, and compliance are paramount. Available across cloud, on-premises, and edge environments, SeekrFlow reduces bias, strengthens data integrity, and simplifies model oversight so organizations can rely on trusted AI decisions in high-stakes settings that impact society’s most sensitive and vital systems. Trusted by leading enterprises and government agencies, we partner with defense, finance, telecom, and critical infrastructure leaders to enable AI solutions that drive real-world results with unmatched transparency and control. We are a team of strategic thinkers and problem-solvers tackling the toughest challenges facing critical infrastructure and global enterprises through best-in-class AI models and customer deployment. Our team operates with unwavering commitment to our core values and mission: We are driven by outcomes—our customers' success is what we strive for every day. We believe trust is earned, which is why we build explainability and transparency into the entire AI lifecycle. We take our responsibility to deliver secure AI seriously. We believe innovation drives progress—we are building the technologies that power the systems our society depends on. Company 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. 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. Important Notice: To conform to U.S. Government international trade regulations, applicant must be a U.S. Citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State or U.S. Department of Commerce. Seekr is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other characteristic protected by applicable law.

Requirements

Must-haves

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.
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.
Clear communication and strong collaboration across technical and non-technical partners.
Reside near Austin, TX or Reston, VA and able to work 3 days per week in office
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.

Nice-to-haves

Preferred Qualifications, Research Scientist-Leaning Candidates:
Master’s or PhD in computer science, machine learning, AI, statistics, or a related field preferred.
Preferred Qualifications, Engineer-Leaning Candidates:
Experience working with GPU/accelerator environments, containerized ML workloads, and production performance tuning; experience with custom GPU/accelerator kernels for AI workload optimization is a plus.
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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningllmmlpythonci/cdapikubernetes

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Parental Leave

Wellness