Research Scientist: Foundational Models for Structured Data

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

Job summary

Stealth Startup in San Francisco seeks a Research Scientist to advance foundation models for tabular data, time series, and broader structured data. You will own the research agenda, design experiments, and write up results for engineers and stakeholders.

You will drive end-to-end pretraining, develop synthetic data strategies, and build robust evaluation systems while staying current with cutting-edge research. This is a high-ownership, high-freedom role in an early-stage environment.

Qualifications

• A strong publication record; PhD preferred but not required.
• Hands-on experience training large-scale models with multi-GPU or multi-node pretraining runs.
• Comfortable reading and modifying attention kernels, developing custom modules, and profiling systems.
• Deep familiarity with transformer internals and architectural trade-offs.
• Strong written and verbal communication skills with cross-functional colleagues.
• Comfortable in an early-stage environment where priorities evolve.

Responsibilities

• Design and run experiments on model architecture, including attention mechanisms and in-context learning for tabular foundation models.
• Own pretraining runs end to end—launch, profile, debug loss curves, and analyze results.
• Design synthetic data generation strategies to control generalization capabilities.
• Build evaluation systems, benchmarks, scoring rules, and perform statistical analyses.
• Extend models to new modalities and translate research into concrete decisions.

Skills

Research

Publications

Communication

Problem solving

Transformer knowledge

Education

PhD preferred

Tools

PyTorch

CUDA

Distributed training

FlashAttention

Job description

Stealth Startup in San Francisco seeks a Research Scientist to advance foundation models for tabular data, time series, and broader structured data. You will own the research agenda, design experiments, and write up results for engineers and stakeholders.

You will drive end-to-end pretraining, develop synthetic data strategies, and build robust evaluation systems while staying current with cutting-edge research. This is a high-ownership, high-freedom role in an early-stage environment.