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 Role We'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 Do

• Design and scale RAG pipelines (vector search, embeddings, retrieval strategy) powering Barkie's on-course voice AI and golf knowledge base
• Build 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 grows
• Build internal and product-facing automations/bots — from QA and testing pipelines to in-app assistants
• Work directly with our Head of AI Product and founding team to take ideas from whiteboard to shipped feature, fast
• Help instrument and improve model performance, latency, and cost across our AI stack

What You Bring

• Real, hands-on experience building with LLMs (OpenAI, Anthropic, Gemini, or similar) in production — not just API calls, but systems thinking around prompts, context, and reliability
• Experience building or scaling RAG systems — you know the tradeoffs between chunking strategies, embedding models, and retrieval methods
• Strong database fundamentals (SQL/Postgres; vector DB experience like Pinecone a big plus)
• Comfort building automations/bots — scripting, workflow orchestration, integrations
• A builder's mindset: comfortable in a startup where you'll wear multiple hats and ship without a lot of hand-holding
• Bonus points: golf knowledge, experience with voice AI/conversational interfaces, or prior startup/0-to-1 product experience

Why Barkie

• Get in early on a product with real users, real traction, and a genuinely novel use of AI in a sport people are obsessed with
• Work directly with the founders — no layers, no bureaucracy
• Meaningful equity in a company built for scale
• Remote-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.