S

Founding Machine Learning Engineer

Stealth Startup • San Francisco Bay Area
Visa Sponsorship Relocation
This Job is No Longer Active This position is no longer accepting applications

We're hiring a Founding Machine Learning Engineer to build production-grade systems combining LLM-powered agents with time-series foundation models. The role involves designing, training, and deploying ML systems, as well as contributing to defining agent interactions with multimodal numerical data. The ideal candidate has 4-10 years of experience as an ML Engineer, Research Engineer, or Applied Scientist.

Key Highlights
Design, train, and deploy production ML systems
Build and scale LLM-powered agents
Apply and extend time-series modeling techniques
Key Responsibilities
Design, train, and deploy production ML systems
Build and scale LLM-powered agents
Apply and extend time-series modeling techniques
Operate end-to-end: from data ingestion and preprocessing to deployment, monitoring, and continuous improvement
Technical Skills Required
Machine Learning Agent Development Time-Series Modeling LLM-powered agents Multi-step reasoning Tool integration Autonomous workflows Memory/context management Adaptive strategies Evaluation frameworks Forecasting Anomaly detection Multimodal fusion MCP A2A CI/CD Observability
Benefits & Perks
Salary
Visa sponsorship
Relocation package
Nice to Have
Experience training custom neural networks beyond pre-trained LLMs
A background in time-series modeling
Published research or open-source contributions in ML/AI

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:

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