AI / Automation engineer - Barkie.ai

Who We Are Barkie.ai is building the first true AI-native golf platform — a voice-first AI caddie that talks to you on the course, a scorecard digitization engine (ScoreShot) that reads your handwriting with 95%+ accuracy, and a betting/gambling engine that makes side games effortless to run and settle.

We're a Google Early Access Partner on the Gemini Live API, we're affiliated with USGA/GHIN, and we're a small, fast-moving team that ships.

Founded by a scratch golfer and former pro athlete alongside a top-100 globally ranked AI expert, we're not building a chatbot bolted onto golf — we're rethinking what an AI caddie in your pocket (or on your wrist) can actually do.

Our team includes engineers and builders with backgrounds from Google, NASA, and Meta.

The RoleWe're looking for an AI (Automation) Engineer to help us scale the systems behind Barkie — RAG pipelines, LLM orchestration, database architecture, and the bots/automations that keep our product and our team moving fast.

This is a hands-on, build-things role for someone who wants their fingerprints on a product real golfers use every weekend.

What You'll DoDesign and scale RAG pipelines (vector search, embeddings, retrieval strategy) powering Barkie's on-course voice AI and golf knowledge baseBuild and refine LLM-driven workflows — prompt architecture, tool use, structured outputs, and hybrid LLM + deterministic logic (especially for gameplay/betting settlement)Architect and optimize databases (Supabase/Postgres, Pinecone) for speed, accuracy, and scale as our user base growsBuild internal and product-facing automations/bots — from QA and testing pipelines to in-app assistantsWork directly with our Head of AI Product and founding team to take ideas from whiteboard to shipped feature, fastHelp instrument and improve model performance, latency, and cost across our AI stack What You BringReal, hands-on experience building with LLMs (OpenAI, Anthropic, Gemini, or similar) in production — not just API calls, but systems thinking around prompts, context, and reliabilityExperience building or scaling RAG systems — you know the tradeoffs between chunking strategies, embedding models, and retrieval methodsStrong database fundamentals (SQL/Postgres; vector DB experience like Pinecone a big plus)Comfort building automations/bots — scripting, workflow orchestration, integrationsA builder's mindset: comfortable in a startup where you'll wear multiple hats and ship without a lot of hand-holdingBonus points: golf knowledge, experience with voice AI/conversational interfaces, or prior startup/0-to-1 product experience Why BarkieGet in early on a product with real users, real traction, and a genuinely novel use of AI in a sport people are obsessed withWork directly with the founders — no layers, no bureaucracyMeaningful equity in a company built for scaleRemote-friendly Interested?Send a note and your background to [email protected] — tell us about something you've built with LLMs or RAG that you're proud of.

Bonus points if you golf.