Minimum qualifications: Bachelor's degree in Computer Science or a related technical field, or equivalent practical experience. 2 years of industry experience as a Research Engineer (RE) or Software Engineer (SWE). 2 years of experience building in Python. 2 years of experience with LLM agents, LLM inference/serving, reinforcement learning for LLMs, or multi-agent orchestration. Experience building 0-to-1 systems, pipelines, and scalable infrastructure. Preferred qualifications: Experience engineering search and retrieval pipelines, time-series workflows, or simulation environments with synthetic data generators. Experience working alongside research teams to convert groundbreaking, open-ended research ideas into robust prototypes and production-grade products. A demonstrated interest in judgemental forecasting, decision-making under uncertainty, prediction markets, or AI safety and calibration. A track record of impactful work, demonstrated through publications, open-source contributions, or shipped products and scalable engineering systems. Expertise in using modern AI tools to design, debug, and build complex software systems efficiently. About the job At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world. From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers. Research Engineers at DeepMind are the bridge between ambitious research ideas and functional, large-scale systems. You will work in close collaboration with Research Scientists to design, build, and scale the infrastructure, models, and tools necessary to drive groundbreaking research in machine forecasting. Acting as a generalist engineer, you will navigate diverse parts of our codebase and pipeline to transform research hypotheses into robust, high-performance experiments. Your work will focus on scaling LLM agent architectures, optimizing inference and serving for complex reasoning workflows, engineering reinforcement learning pipelines, and building contamination-immune evaluation platforms. Your technical contributions will enable the team to make rapid, empirically driven progress toward autonomous systems that can reliably anticipate future events and reason under uncertainty. Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority. We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort. Responsibilities Design, build, and own 0-to-1 infrastructure for frontier forecasting systems, multi-agent workflows, and structured reasoning pipelines. Develop and scale training and inference pipelines, implementing novel reinforcement learning methods, process-based reward loops, and self-improvement algorithms. Build robust tooling and retrieval harnesses, enabling agents to navigate temporal data, execute code sandboxes, and filter unstructured information without temporal leakage. Engineer contamination-immune evaluation platforms to test model calibration, logical coherence across beliefs, and performance in simulated or live environments. Leverage and integrate modern AI coding tools (e.g., AGY, Claude Code, Cursor) to accelerate development cycles and rapidly robustify experimental research prototypes into production-ready systems.