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