Founding Machine Learning Engineer [33116]

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 strategiesDevelop and refine evaluation frameworks for agents, ensuring reliability, safety, and measurable performanceApply and extend time-series modeling techniques (forecasting, anomaly detection, multimodal fusion) in real-world customer scenariosOperate end-to-end: from data ingestion and preprocessing to deployment, monitoring, and continuous improvementStay 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 systemsHands-on expertise in LLM-powered agents: multi-step reasoning, tool use, context windows, autonomous workflows, agent memoryDeep 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, observabilityComfort 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 & SponsorshipLocation: San Francisco Bay Area, CA (in-person)Visa Sponsorship: H1-B, O1

Created: 2026-05-13