Ryan Swearingen

Chief Technology Officer @ Anhedral

Mountain View, CA & Bend, OR

(650) 963-6629 · ryan@anhedral.com

LinkedIn: https://www.linkedin.com/in/ryan-swearingen/

Ryan Swearingen

SUMMARY

Founder and technology leader who connects company strategy with product and engineering execution. Leads teams and platforms from concept through secure, reliable production, with hands-on depth across AI systems, application development, infrastructure, and edge computing. A systems generalist and lifelong builder who makes pragmatic decisions balancing speed, customer value, operational risk, and long-term leverage.

EXPERIENCE

Anhedral

Anhedral

Chief Technology Officer

2025 - Present (Full-time)

  • Co-founded Anhedral and leads development of Anhedral Core, the platform underpinning the company's agent-driven products.
  • Owns technical strategy, architecture, and engineering execution across product lines, translating product goals into reliable systems.
  • Builds autonomous agents that operate software interfaces on users' behalf, with an emphasis on dependable execution and recoverable workflows.

Tech: Computer Using Agents, autonomous systems, full-stack development, platform architecture

Cognition Labs

Cognition Labs

Lead Software Engineer

2025 - Present (Full-time)

  • Leads engineering for CogniLens across iOS, Android, web, browser extension, and AR/XR experiences for neurodiverse readers.
  • Owned end-to-end system architecture across frontend clients, backend APIs, authentication, role-based access control, subscriptions, and relational data models.
  • Architected and shipped a developer-facing API enabling internal and external integrations while enforcing tenant isolation and security boundaries.
  • Serves as technical owner across engineering, product, and research, driving scope, prioritization, and delivery from concept through production.
  • Translated neurodiversity accessibility research into shipped, user-facing functionality used in production environments.
  • Supported production releases, CI/CD pipelines, and post-launch iteration, incorporating real-world usage signals to guide ongoing improvements.

Tech: Cross-platform mobile & web stacks, cloud APIs, relational databases, authentication & payments infrastructure, CI/CD, modern UI systems

Neesh AI

LearnAIR (formerly Neesh AI)

Lead Software Engineer

2024 - 2025 (Full-time)

  • Directed the end-to-end development and launch of a B2B AI chat platform adopted by paying customers.
  • Architected a provider-agnostic, multi-LLM system, dynamically selecting models based on cost, latency, and capability constraints.
  • Built retrieval-augmented generation (RAG) pipelines including document ingestion, chunking, embeddings, retrieval, and user-level personalization.
  • Delivered core platform features including chat history, artifacts, folder hierarchies, role-based access control, subscriptions, and image generation.
  • Led and mentored a small engineering team, setting technical direction, reviewing code, and unblocking delivery.
  • Owned production reliability and iteration cadence, prioritizing features based on direct customer feedback and operational needs.

Tech: LLM application architecture, RAG systems, full-stack web development, workflow automation, team-scale engineering practices

SAP

SAP

Software Engineer

2024 (Contract)

  • Developed a full-stack AI knowledge graph application using structured LLM outputs and deterministic pipelines.
  • Collaborated with design and engineering teams to translate research goals into a functional production prototype.
  • Conducted UX studies and iterated on system behavior based on qualitative user feedback.
  • Contributed to work published in an IEEE research paper, bridging applied research and real-world system implementation.

Tech: Structured LLM pipelines, data-centric design, full-stack web systems, research-driven workflows

DXM

DXM

Software Engineer

2014 - 2025 (Part-time)

  • Long-term contributor (10+ years) to DXM's AI and creative technology initiatives alongside academic and professional roles.
  • Helped build an AI image generation platform serving brand and creative customers.
  • Implemented and evaluated diffusion models and advanced prompt-engineering workflows.
  • Supported early-stage startup efforts through rapid prototyping, demos, and technical direction during fundraising and customer pitches.

Tech: Generative image systems, diffusion models, prompt engineering, rapid prototyping

Ford Motor Company

Ford Motor Company

Intern - UXD

2019 (Internship)

  • Built simulation tooling using GTA V modification frameworks to model autonomous driving scenarios for AV research.
  • Created data pipelines for video transcription, labeling, and analysis to support research workflows.
  • Operated in a research-focused environment emphasizing reproducibility, data quality, and experimentation rigor.

Tech: Simulation environments, data annotation pipelines, research tooling

EDUCATION

University of Oregon

University of Oregon

Bachelor of Science, Multidisciplinary Science

2020 - 2025

Completed degree while working concurrently in full-time and contract software engineering roles.

Switched majors during senior year from Cellular, Molecular & Developmental Biology to Multidisciplinary Science.

SKILLS

Core Competencies

  • Full-stack engineering across web, mobile, and APIs
  • Production AI and LLM application architecture
  • System design, scalability, and tradeoff analysis
  • Authentication, RBAC, subscriptions, and payments
  • Technical leadership, mentorship, and code review

Technologies & Platforms

  • Frontend & mobile: modern web frameworks and cross-platform mobile stacks
  • Backend & data: cloud APIs, relational databases, object storage, vector stores
  • Infrastructure: Linux VPS hosting, containers, serverless deployment, CI/CD, CDNs, DNS, and TLS
  • Systems: local development, edge hardware, and cross-platform environments

AI / LLM Systems

  • Provider-agnostic multi-LLM integrations
  • RAG, document ingestion, embeddings, and retrieval
  • Structured outputs, tool calling, and agent workflows
  • Evaluation and cost, latency, and reliability optimization

ADDITIONAL EXPERIENCE

  • University of Oregon Bioinformatics Interest Group
  • UC Berkeley SPLASH Participant
  • Stanford Code Sleep Repeat Participant