Founding Machine Learning Engineer [32913]

Stealth Startup

United States

United States

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About

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 Role This 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 iteration What 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 ambiguity Nice 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/AI Location & Sponsorship Location:
San Francisco Bay Area, CA
(in-person) Visa Sponsorship:
H1-B, O1

United States

Languages

English

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