Founding Software Engineer [33460]

We're hiring our Founding Machine Learning Engineer (MLE) with expertise in Agent Development and Time-Series Modeling. You'll play a foundational role in building production-grade systems that combine the power of LLM-powered agents with time-series foundation models.The RoleThis is not a narrow research role — you'll design, train, deploy, and monitor ML systems end-to-end, moving from prototype to production with speed and autonomy. You'll also be a core contributor to defining how agents interact with multimodal numerical data, a problem space where the playbook does not yet exist.Job Description: • Design, train, and deploy production ML systems (LLM-powered agents + time-series models) • Build and scale LLM-powered agents with advanced capabilities: multi-step reasoning, tool integration, autonomous workflows, memory/context management, and adaptive strategies • Develop and refine evaluation frameworks for agents, ensuring reliability, safety, and measurable performance • Apply and extend time-series modeling techniques (forecasting, anomaly detection, multimodal fusion) in real-world customer scenarios • Operate end-to-end: from data ingestion and preprocessing to deployment, monitoring, and continuous improvement • Stay ahead of the curve on the latest innovations in AI agents, orchestration frameworks, and infrastructure (MCP, A2A, etc.) • Partner directly with researchers, engineers, and lighthouse customers to validate solutions and drive rapid iterationWhat we're looking for: • Proven industry experience (4-10 years) as an ML Engineer, Research Engineer, or Applied Scientist, with a track record of shipping production ML systems • Hands-on expertise in LLM-powered agents: multi-step reasoning, tool use, context windows, autonomous workflows, agent memory • Deep understanding of agent evaluation techniques (reliability, safety, success metrics) • Up-to-date with modern agent infrastructure and frameworks (MCP, A2A, etc.) • Fluency with ML engineering best practices: reproducibility, monitoring, scaling, CI/CD, observability • Comfort operating in a fast-paced startup: shipping quickly, making tradeoffs, and thriving in ambiguityNice to have: • Experience training custom neural networks beyond pre-trained LLMs (e.g., transformers for time-series or multimodal data) • A background in time-series modeling (forecasting, anomaly detection, classical + deep learning approaches) • Published research or open-source contributions in ML/AILocation & Sponsorship • Location: San Francisco Bay Area, CA (in-person) • Visa Sponsorship: H1-B, O1