ibarrajo · HackerTrans

vor 10 Tagendiscuss

  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

ibarrajo

vor 14 Tagendiscuss

I’m sitting on a 0-day rce on the tizen browser (smart tv)

Didn’t bother submitting since who actually uses tizen?

ibarrajo

vor 22 Tagendiscuss

You can already do this, it’s called reticulum.

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.

ibarrajo

letzten Monatdiscuss

  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

I build agent systems. A decade in production distributed systems (Uber, DoorDash, Jobscan); the last year spent designing and shipping autonomous, LLM-driven infrastructure. Looking for a hands-on senior IC role on an AI-infra / agent-platform / applied-AI team.

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.

ibarrajo

vor 2 Monatendiscuss

  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

Senior backend / platform IC, 10+ years on distributed systems at Uber, DoorDash, Jobscan. Looking for a hands-on senior IC role — infrastructure, developer platforms, or applied AI systems.

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.

ibarrajo

vor 3 Monatendiscuss

  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

Senior backend/platform engineer, 10+ years building distributed systems at scale (Uber, DoorDash). Looking for a hands-on senior/staff IC role — infrastructure, developer platforms, or applied AI systems.

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.

ibarrajo

vor 4 Monatendiscuss

  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

Senior backend/platform engineer with 10+ years building distributed systems at scale (Uber, DoorDash). Looking for a hands-on IC role on a strong team — infrastructure, developer platforms, or applied AI systems.

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.

ibarrajo

vor 5 Monatendiscuss

  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

I'm a backend/platform engineer and developer advocate with over 10 years of experience building systems at scale across startups and growth-stage companies like Uber and DoorDash.

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.

ibarrajo

vor 7 Monatendiscuss

  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

I'm a backend/platform engineer and developer advocate with over 10 years of experience building systems at scale across startups and growth-stage companies like Uber and DoorDash.

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.

ibarrajo

vor 8 Monatendiscuss

I just had to try, got a bitmap and set it directly on the canvas using js, I wonder if it will print.

Canvas is 348x348.

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

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