What i found is that it is amazing once you determine and the invariants that are essential to the guarantees you want to keep.

I built my own formally verified workflow engine, it was easy but mostly because i already knew the pitfalls and the foundational pillars of Cadence and Temporal.

Also, it doesnt seem like common knowledge, but you can export libraries that compile to C from lean. With them you do get performant code that that has been verified and easily call them as C bindings from elsewhere.

Lean itself does not have a good IO stack in general but its good enough for small projects.

There is a caveat to exporting libs or native_decide in general. Once you export into C, ABI its now outside of the scope of the Lean kernel which means that bugs can creep in from the compiler itself.

  Location: Seattle, WA
  Remote: Yes
  Willing to relocate: Yes (SF, NYC for the right team)
  Technologies: Go, Python, Rust, TypeScript, Kotlin | LLM agents, multi-agent orchestration, MCP, RAG | Postgres, Redis, Kafka, gRPC, Kubernetes | AWS, GCP, Cloudflare | Cadence/Temporal, Playwright/CDP
  Résumé: https://elninja.com/resume
  LinkedIn: https://www.linkedin.com/in/elninja
  GitHub: https://github.com/ibarrajo
  Email: alex [at] elninja.com

Background: at Uber, worked on Cadence (open-source workflow engine, 12B+ workflows/month internally) — executed multi-region failovers on domains doing billions of ops/day, shipped non-determinism detection via shadow replay, rebuilt cadenceworkflow.io, onboarded 50+ teams. At DoorDash, refund rules engine for McDonald's/Chipotle (500K refunds/month). At Jobscan, Senior Software Engineer: 97% → 99.99% uptime through an AWS migration, $475K/yr recovered via payment A/B testing. I've operated durable-execution systems at scale and have opinions about where that paradigm earns its keep and where autonomous agents are the better tool — which is what I build now.

The issue that remains are two things, ensuring the idea of the proof is actually the thing you want to prove and the interpretation of the results you get. But besides that, everything inside of the kernel checked code is logically consistent

It’s surprisingly easy to do with AI. The hard part has been manually verifying and validating the results. I took one of the smaller findings (disproving a conjecture) and wrote a paper as my first endeavor into publishing.

Because the next few findings i have in the pipeline are substantial in the field of quantum topology and physics im taking some time to publish them with a ton of scrutiny. And verification has taken more time than it did to make the discoveries.

Here’s my first piece if anybody is interested in number theory: https://arxiv.org/abs/2607.09793

  Location: Seattle, WA
  Remote: Yes
  Willing to relocate: Yes (SF, NYC for the right team)
  Technologies: Go, Python, Rust, TypeScript, Kotlin | LLM agents, multi-agent orchestration, MCP, RAG, evals, GPTQ/quantization, Triton | Postgres, Redis, Kafka, gRPC, Kubernetes | AWS, GCP, Cloudflare | Cadence/Temporal, Playwright/CDP
  Résumé: https://elninja.com/resume
  LinkedIn: https://www.linkedin.com/in/elninja
  GitHub: https://github.com/ibarrajo
  Email: alex [at] elninja.com

Background: at Uber, worked on Cadence (open-source workflow engine, 12B+ workflows/month internally) — executed multi-region failovers on domains doing billions of ops/day, shipped non-determinism detection via shadow replay, rebuilt cadenceworkflow.io, onboarded 50+ teams. At DoorDash, refund rules engine for McDonald's/Chipotle (500K refunds/month). At Jobscan, Senior Software Engineer: 97% → 99.99% uptime through an AWS migration, $475K/yr recovered via payment A/B testing. I've operated durable-execution systems at scale and have opinions about where that paradigm earns its keep and where autonomous agents are the better tool — which is what I build now.

Didn’t bother submitting since who actually uses tizen?

Essentially it’s encrypted internet/networking over any type of network including LoRa.

Issue is the size of the community and linking up to actually serve internet or surface public services there.

  Location: Seattle, WA
  Remote: Yes
  Willing to relocate: Yes (SF, NYC for the right team)
  Technologies: Go, Python, Rust, TypeScript, Kotlin | LLM agents, multi-agent orchestration, MCP, RAG, evals, GPTQ/quantization, Triton | Postgres, Redis, Kafka, gRPC, Kubernetes | AWS, GCP, Cloudflare | Cadence/Temporal, Playwright/CDP
  Résumé: https://elninja.com/resume
  LinkedIn: https://www.linkedin.com/in/elninja
  GitHub: https://github.com/ibarrajo
  Email: alex [at] elninja.com

Background: at Uber, worked on Cadence (open-source workflow engine, 12B+ workflows/month internally) — executed multi-region failovers on domains doing billions of ops/day, shipped non-determinism detection via shadow replay, rebuilt cadenceworkflow.io, onboarded 50+ teams. At DoorDash, refund rules engine for McDonald's/Chipotle (500K refunds/month). At Jobscan, Interim Head of Eng: 97% → 99.99% uptime through an AWS migration, $475K/yr recovered via payment A/B testing. I've operated durable-execution systems at scale and have opinions about where that paradigm earns its keep and where autonomous agents are the better tool — which is what I build now.

Shipped this past year:

- Maquina: a structured "cognitive language" + control plane for coordinating heterogeneous LLM agents. Full EBNF grammar, an evaluation harness, and a runtime that treats multi-agent coordination as a first-class protocol instead of glue code.

- Meridian: 29k-line TypeScript autonomous goal-graph executor (Postgres + MCP) that decomposes high-level charters into plan nodes and runs LLM-backed agents on a live dashboard, with budget circuit-breakers and governance enforcement.

- Pursuit (co-founder, AI Scalathon Seattle 2026): agent-to-agent recruiting — a 3-min structured interview producing ranked multi-dimensional match scores. Ran live: 51 reports + 120 real matches against Microsoft, JPMorgan, Uber, CoreWeave, Adobe, Block, Boeing, Lululemon, SpaceX.

- ApplyPilot (OSS, github.com/ibarrajo/ApplyPilot): multi-provider LLM orchestration with cost-aware routing across Gemini/OpenAI/Anthropic; Claude Code + Playwright automation across 40+ ATS workflows.

- OpenAI Parameter Golf: 14+ PRs to the 16MB / 10-min / 8×H100 LLM-training challenge (best valid submission val_bpb 1.1354). Hands-on with Int5/6 GPTQ, score-first test-time training, Triton kernels, and Hessian-based calibration.

I want a team where the hard problems are agents, evals, and inference — and shipping daily is the norm.

  Location: Seattle, WA
  Remote: Yes
  Willing to relocate: Yes (SF, NYC for the right team)
  Technologies: Go, Python, Kotlin, TypeScript, Rust | Cadence/Temporal, Kafka, gRPC, Postgres, Redis, Kubernetes | AWS, GCP, Cloudflare | LLM agents, MCP, Playwright/CDP, RAG
  Résumé: https://elninja.com/resume
  LinkedIn: https://www.linkedin.com/in/elninja
  GitHub: https://github.com/ibarrajo
  Email: alex [at] elninja.com

At Uber, Developer Advocate for Cadence — the open-source workflow engine running 12B+ workflows/month internally. Onboarded 50+ teams, executed multi-region failovers on domains processing billions of daily ops, rebuilt cadenceworkflow.io, and shipped non-determinism detection via shadow replay. Before Uber: refund rules engine for McDonald's/Chipotle at DoorDash (500K refunds/month), and Interim Head of Engineering at Jobscan (97% → 99.99% uptime on AWS migration, $475K/yr recovered via payment A/B testing).

Currently shipping (June 2025 → now):

- Pursuit (co-founder, AI Scalathon Seattle 2026): agent-to-agent recruiting — a 3-min structured candidate/employer interview producing ranked multi-dimensional match scores. Ran live: 51 reports + 120 real matches against Microsoft, JPMorgan, Uber, CoreWeave, Adobe, Block, Boeing, Lululemon, SpaceX.

- Meridian: 29k-line TypeScript autonomous goal-graph executor (Postgres + MCP) that decomposes high-level charters into plan nodes and runs LLM-backed agents on a real-time dashboard.

- ApplyPilot (OSS, github.com/ibarrajo/ApplyPilot): multi-provider LLM orchestration with cost-aware routing across Gemini/OpenAI/Anthropic; Claude Code + Playwright integration across 40+ ATS workflows.

- OpenAI Parameter Golf: 14+ PRs opened to the 16MB / 10-min / 8xH100 LLM-training challenge. Hands-on with Int5/6 GPTQ, test-time training with score-first backprop, Triton kernels, Hessian-based calibration.

Looking for a team where shipping daily is the norm.

  Location: Seattle, WA
  Remote: Yes
  Willing to relocate: Yes (SF, NYC for the right role)
  Technologies: Go, Python, Kotlin, TypeScript | Cadence/Temporal, Kubernetes, Kafka, Postgres, Redis | GCP, AWS | LLM agents, workflow orchestration
  Résumé/CV: https://elninja.com/resume
  LinkedIn: https://www.linkedin.com/in/elninja
  Email: alex [at] elninja.com
  GitHub: https://github.com/ibarrajo

At Uber I was Developer Advocate for Cadence, an open-source workflow orchestration engine powering 12B+ workflows/month. Drove adoption across dozens of teams, debugged production issues at scale, and integrated Cadence into Uber's Michelangelo ML platform. Fine-tuned LLMs on internal corpora and built classification systems for developer support triage.

Currently building: an autonomous personal operating system with LLM-driven agent orchestration (graph-based scheduling, MCP integrations), an AI-powered job application pipeline, and a consumer AI product. Shipping code daily and looking for a team where that energy is the norm.

  Location: Seattle, WA
  Remote: Yes
  Willing to relocate: Yes (SF, NYC for the right role)
  Technologies: Go, Python, Kotlin, TypeScript | Cadence/Temporal, Kubernetes, Kafka, Postgres, Redis | GCP, AWS | LLM integrations, workflow orchestration
  Résumé/CV: https://elninja.com/resume
  LinkedIn: https://www.linkedin.com/in/elninja
  GitHub: https://github.com/ibarrajo
  Email: alex [at] elninja.com

Most recently at Uber as Developer Advocate for Cadence, an open-source workflow orchestration engine powering 12B+ workflows/month. Partnered with engineering teams across the org to drive adoption, debug production issues, and scale fault-tolerant pipelines — including integration with Uber's Michelangelo ML platform. Fine-tuned LLMs on internal corpora and built classification systems for developer support triage.

Since then I've been building: a self-orchestrating workflow engine with LLM-driven agents, AI-powered job automation pipelines, and a couple of side projects — El Minarete (https://elminarete.com), a Lotería card game generator, and Lomito (https://lomito.org), a civic platform for animal welfare in Mexico. Recently pitched Pursuit at the Venture Mechanics' AI Scalathon — an agent-to-agent recruiting platform where AI interviews AI to match candidates to roles.

Previously: Kotlin microservices migration and refund rules engine at DoorDash. NLP resume-matching pipelines at Jobscan. Founded a healthtech startup (CTO).

Interested in: infrastructure, developer tooling, workflow orchestration, applied ML/AI — especially in healthcare, logistics, finance, or defense. I want to build systems that matter with people who care about craft.

  Location: Seattle, WA
  Remote: Yes
  Willing to relocate: Yes, open to SF, NYC for the right role
  Technologies: Go, Kotlin, Python, TypeScript, PHP, Java, Cadence Workflows, Kubernetes, GCP, AWS, Postgres, Redis, Kafka, Docker
  Résumé/CV: https://elninja.com/resume
  LinkedIn: https://www.linkedin.com/in/elninja
  Email: josue [at] elninja.com

Until earlier this year, I worked at Uber as a Developer Advocate for Cadence — an open-source workflow orchestration engine powering 12B+ workflows/month. I partnered with dozens of teams to support adoption, scale distributed systems, and troubleshoot production issues. Cadence is a key component of Uber's Michelangelo ML platform, and I worked closely with AI/ML teams to integrate it into fault-tolerant pipelines. I also supported internal model training efforts, including fine-tuning LLMs to answer Cadence-related questions and building systems to classify and prioritize developer support.

At DoorDash, I contributed to the migration from Django to Kotlin microservices on the Drive Merchant Services team, focusing on customer support endpoints. I also designed and implemented a modular refund rules engine for our largest partners, enabling flexible refund logic and supporting internal tooling.

I’ve previously built NLP pipelines at Jobscan to match résumés to job descriptions and score resume strength, and earlier, trained models to predict ad performance based on copywriting—my first foray into ML back in 2016.

After wrapping up at Uber, I'm now excited to return to a hands-on engineering role with a collaborative, technically strong team and a path toward engineering leadership (EM or CTO). I'm particularly interested in infrastructure, developer platforms, and applied ML systems in domains like healthcare, logistics, finance, or defense.

Outside of work, I enjoy navigating Puget Sound on my sailboat, wrenching on my motorcycle, and getting curious about obscure systems and trivia. Let’s build something meaningful.

  Location: Seattle, WA
  Remote: Yes
  Willing to relocate: Yes, open to SF, NYC for the right role
  Technologies: Go, Kotlin, Python, TypeScript, PHP, Java, Cadence Workflows, Kubernetes, GCP, AWS, Postgres, Redis, Kafka, Docker
  Résumé/CV: https://elninja.com/resume
  LinkedIn: https://www.linkedin.com/in/elninja
  Email: josue [at] elninja.com

Until earlier this year, I worked at Uber as a Developer Advocate for Cadence — an open-source workflow orchestration engine powering 12B+ workflows/month. I partnered with dozens of teams to support adoption, scale distributed systems, and troubleshoot production issues. Cadence is a key component of Uber's Michelangelo ML platform, and I worked closely with AI/ML teams to integrate it into fault-tolerant pipelines. I also supported internal model training efforts, including fine-tuning LLMs to answer Cadence-related questions and building systems to classify and prioritize developer support.

At DoorDash, I contributed to the migration from Django to Kotlin microservices on the Drive Merchant Services team, focusing on customer support endpoints. I also designed and implemented a modular refund rules engine for our largest partners, enabling flexible refund logic and supporting internal tooling.

I’ve previously built NLP pipelines at Jobscan to match résumés to job descriptions and score resume strength, and earlier, trained models to predict ad performance based on copywriting—my first foray into ML back in 2016.

After wrapping up at Uber. I'm now excited to return to a hands-on engineering role with a collaborative, technically strong team and a path toward engineering leadership (EM or CTO). I'm particularly interested in infrastructure, developer platforms, and applied ML systems in domains like healthcare, logistics, finance, or defense.

Outside of work, I enjoy navigating Puget Sound on my sailboat, wrenching on my motorcycle, and getting curious about obscure systems and trivia. Let’s build something meaningful.

Canvas is 348x348.

https://gist.github.com/ibarrajo/d5dc106cf226a0f2286e7e959ce...

  Location: Seattle, WA
  Remote: Yes
  Willing to relocate: No
  Technologies: Python, Kotlin, Java, PHP, Laravel, Postgresql, Mysql, AWS (EC2, RDS, ECS, Lambda, etc..), Linux, Kubernetes, Kafka, VueJS, Angular, Node, Typescript, Javascript, full stack
  Résumé/CV: https://docs.google.com/document/d/1tYBr4v7HUme1bL7p_ksiFljiEM21Jccs/edit?usp=sharing&ouid=106145294552267002206&rtpof=true&sd=true
  Email: ibarra.josue91 at gmail
  LinkedIn: https://www.linkedin.com/in/elninja/

We are looking for a Full-Stack Engineer to help build the next generation of job search tools. Our agile development team develops and supports our products top-to-bottom and ship software on a regular basis. A successful candidate will thrive in a fast paced, start-up team environment and is passionate about building software the right way. You’ll be working alongside our engineers, CEO and CTO collaborating on the various layers of the infrastructure to help scale and grow the company.

Director of Engineering:

https://jobs.lever.co/jobscan-2/2de25aed-aed2-460d-8a09-b116...

Senior Software Engineer:

https://jobs.lever.co/jobscan-2/dc22f8bc-571b-45df-9a63-4614...

Jobscan is a fast-growing start-up in the heart of historic Pioneer Square, Seattle, WA. Jobscan is web service that helps job seekers land more interviews by using AI to analyze one’s resume or LinkedIn profile against any job description. We're building the next generation of job search tools to make job search easier. We’re user-funded, profitable, and growing exponentially.

We are looking for a Full-Stack Engineer to help build the next generation of job search tools. Our agile development team develops and supports our products top-to-bottom and ship software on a regular basis. A successful candidate will thrive in a fast paced, start-up team environment and is passionate about building software the right way. You’ll be working alongside our engineers, CEO and CTO collaborating on the various layers of the infrastructure to help scale and grow the company.

Director of Engineering:

https://jobs.lever.co/jobscan-2/2de25aed-aed2-460d-8a09-b116...

Senior Software Engineer:

https://jobs.lever.co/jobscan-2/dc22f8bc-571b-45df-9a63-4614...

Jobscan is a fast-growing start-up in the heart of historic Pioneer Square, Seattle, WA. Jobscan is web service that helps job seekers land more interviews by using AI to analyze one’s resume or LinkedIn profile against any job description. We're building the next generation of job search tools to make job search easier. We’re user-funded, profitable, and growing exponentially.

We are looking for a Full-Stack Engineer to help build the next generation of job search tools. Our agile development team develops and supports our products top-to-bottom and ship software on a regular basis. A successful candidate will thrive in a fast paced, start-up team environment and is passionate about building software the right way. You’ll be working alongside our engineers, CEO and CTO collaborating on the various layers of the infrastructure to help scale and grow the company.

https://jobs.lever.co/jobscan-2/70306af6-7f25-428c-8635-a3ff...