S

Seekr

AI-driven reliability scoring of online content

Senior AI Infrastructure Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Five to eight years of professional software engineering experience building distributed systems, cloud infrastructure, or large-scale platform services.
  • Strong production machine learning infrastructure experience, ability to execute complex work independently, and ownership of significant components.
  • A four-year or higher degree, or additional relevant experience, in addition to years of work experience.
  • Demonstrated success designing and operating production Kubernetes environments supporting cloud-native applications and distributed services.
  • Strong software engineering skills using Python and one or more modern programming languages such as Go, Rust, or C++.
  • Proven ability to design, build, and operate production artificial intelligence or machine learning infrastructure.
  • Expertise developing and optimizing large-scale artificial intelligence inference platforms, including GPU utilization, distributed inference, batching, caching, quantization, and accelerator performance.
  • Familiarity with modern artificial intelligence serving technologies such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, Ray Serve, or similar platforms.
  • Knowledge of distributed computing, networking, storage systems, cloud-native architectures, and infrastructure automation using technologies such as Kubernetes, Helm, Argo CD, Docker, Prometheus, Grafana, OpenTelemetry, and Infrastructure-as-Code tools.
  • Experience developing enterprise artificial intelligence platforms, autonomous agents, or multi-agent systems, including orchestration, tool execution, governance, observability, and evaluation.
  • Familiarity with event-driven architectures, distributed messaging systems, and public cloud platforms including AWS, Azure, Oracle Cloud Infrastructure, or Google Cloud Platform.
  • Demonstrated technical leadership, including driving architectural decisions, mentoring engineers, and leading complex technical initiatives across cross-functional teams.
  • Demonstrated ability to analyze, profile, and optimize artificial intelligence systems for performance, scalability, reliability, and cost across distributed compute environments.

Responsibilities

  • Design, develop, deploy, and maintain production artificial intelligence infrastructure supporting model training, fine-tuning, inference, evaluation, and agentic artificial intelligence workloads.
  • Design and operate scalable Kubernetes-based infrastructure supporting GPU-accelerated workloads across cloud, on-premises, hybrid, and edge environments.
  • Architect and optimize high-performance inference platforms capable of serving models ranging from resource-constrained edge deployments to trillion-parameter foundation models, focusing on latency, throughput, scalability, reliability, and cost efficiency.
  • Build and maintain distributed systems that enable reliable scheduling, orchestration, deployment, monitoring, and lifecycle management of artificial intelligence workloads.
  • Develop enterprise platforms supporting autonomous and multi-agent artificial intelligence systems, including secure tool execution, orchestration, memory, evaluation, governance, and observability.
  • Design, implement, and automate artificial intelligence infrastructure using Infrastructure-as-Code, GitOps, continuous integration and continuous delivery pipelines, and modern software engineering practices.
  • Evaluate and integrate emerging artificial intelligence infrastructure technologies, model-serving frameworks, hardware accelerators, and cloud-native platforms to improve platform performance, scalability, and reliability.
  • Collaborate with engineering, research, product, and cross-functional teams to deliver secure, scalable, and production-ready artificial intelligence platforms.
  • Lead technical design discussions, perform architecture reviews, mentor engineers, and establish engineering standards and best practices across the AI Infrastructure organization.
  • Participate in production support activities, including troubleshooting complex distributed systems, performance tuning, incident response, and continuous operational improvement.

About the Company

Seekr

AI-driven reliability scoring of online content

Seekr provides AI-based tools to rate the reliability of online content and reveal its political bias. It delivers the Seekr Score, a reliability rating, plus a Political Lean Indicator, Direct Quotes, and Filters to help users find high-quality information quickly through a privacy-first mobile browser. The AI is trained by journalists and data scientists to analyze content efficiently. Compared with competitors, Seekr combines a trust score with political bias signals and quote-based search features in a privacy-focused mobile app, serving individuals and organizations that need verified information. Its goal is to help users navigate the web, reduce misinformation, and access trustworthy information more efficiently.

Company size
51-200
Company stage
Series B
Total funding
$173M
Headquarters
Reston, VA
Founded
2021

Recently Posted Jobs

Explore more opportunities at Seekr

See more jobs