Job Opportunities API

AI / ML Software Engineer. London About Antare Join Antare as we go to market. We’ve been busy building the physical security intelligence platform of the future. Our always-connected platform automatically detects risks, captures evidence and reports on real-world events as they happen, delivering intelligent insights through an intuitive user interface, all hosted in the cloud. Our product is designed so commercial organisations - including, but not limited to, private security, retail, and hospitality - never miss an incident. The Antare founding team of product designers and engineers have together built companies that have been collectively acquired for billions of dollars: We have a genuine level of success under our belts that you could help to contribute to. Today, we’re a growing, hands-on team spanning product, design, finance, marketing, sales, hardware, and software engineering. What we can offer you This is a rare chance to join the early stages of a startup - where your curiosity, ideas, and input will directly influence the product roadmap. You’ll be working closely with a small, experienced engineering team, building real features in a modern frontend codebase from day one. We care deeply about helping early-career engineers grow fast and with confidence. You’ll benefit from hands-on mentorship, regular feedback, and the chance to pair with engineers who’ve built products at scale. It’s a high-trust, low-ego environment designed to help you accelerate your skills while contributing meaningfully. We’re using modern technology stacks across our infrastructure and frontend, and AI-assisted tooling to move fast and stay focused - and you’ll be an integral part of shaping both the user experience and the engineering foundations as we scale. Role Overview You'll work on the AI/ML systems that turn raw audio and video into useful, structured output - and increasingly into agents and analytics that act on it. The day-to-day is a mix of: Prompt and schema engineering against frontier multimodal models - getting reliable structured output from messy real-world data, diagnosing failure modes, and iterating on prompts that survive model upgrades. Agentic systems - designing and building agents that plan, use tools, and reason over our data. Realtime model optimization - working with high performance efficient models for edge analytics, filtering, understanding and event extraction Retrieval and semantic search - building the indexing, embeddings, and retrieval layers that make our RAG and agent systems actually useful, not just demo-grade. Computer vision - object detection, recognition, and other CV techniques applied to our video data, alongside (and sometimes instead of) multimodal LLMs. Statistical analysis and insight generation - turning the structured output our pipelines produce into trends, summaries, and product-surfaced insights. Picking the right tool for the question: a query, a classical model, an LLM, or a hybrid. Evaluation - labelled datasets, judge setups, regression tracking. A lot of the work is making "is this actually good?" answerable. The backend and infrastructure that holds it together - building the cloud services that run the pipeline reliably at scale. Audio and video understanding - transcription, speaker work, event detection, and the narrative/structured outputs the product surfaces. Some lower-level work in C/C++ and firmware where models need to run on-device under real-time constraints. The mix shifts week to week. We're small, so you'll move between prompt iteration, backend code, eval tooling, retrieval/agent work, and looking at real data to understand why something went wrong. What we’re looking for A few years of commercial (or equivalent) experience shipping ML-driven features in production - cloud, embedded, or both. Our stack spans Go (backend and ML orches