Founding Machine Learning Engineer [32934]

Stealth Startup; San Francisco (CA); On-site; USD 180,000 - 280,000; Full time

Job summary

Stealth Startup is seeking a Founding Machine Learning Engineer to design, train, and deploy production ML systems that fuse LLM-powered agents with time-series models. You’ll own end-to-end development from prototype to production and set the standard for agent interaction with multimodal data.

You’ll work closely with researchers and engineers to ship fast, iterate rapidly, and build scalable, reliable AI systems in a fast-paced startup

Qualifications

• 4–10 years of industry experience as ML Engineer, Research Engineer, or Applied Scientist.
• 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.

Responsibilities

• 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

Skills

LLM-powered agents

Time-series modeling

Agent development

Multimodal data

Deployment & monitoring

Autonomous workflows

Tools

MCP

A2A

CI/CD

Job description

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