Application Guide
How to Apply for Research Scientist
at FutureSearch
🏢 About FutureSearch
FutureSearch is a cutting-edge startup building AI systems that reason about the future reliably, a niche yet critical capability for strategic decision-making. Working here means tackling fundamental challenges in AI epistemics—how AI knows and validates information about the future—in a remote-first, research-driven environment.
About This Role
As a Research Scientist, you'll advance the frontier of AI forecasting by evaluating and improving large language models (LLMs) for web research and agentic reasoning. Your work will directly shape benchmarks and datasets that define how AI systems are assessed, with high impact on both the company's products and the broader AI community.
💡 A Day in the Life
Mornings might involve reviewing recent LLM papers or running experiments on a new evaluation metric. Afternoons could be spent analyzing results, writing code for a benchmark, or collaborating with the team on designing a study to test an agentic system's forecasting accuracy.
🚀 Application Tools
🎯 Who FutureSearch Is Looking For
- Has a strong publication record in ML/NLP, particularly on LLM evaluation, reasoning, or knowledge assessment.
- Hands-on experience building and analyzing benchmarks for LLMs (e.g., BIG-Bench, HELM, or custom evaluation suites).
- Proficient in Python and familiar with experiment tracking tools (e.g., MLflow, Weights & Biases) for reproducible research.
- Deeply curious about AI epistemics—e.g., how LLMs handle uncertainty, calibration, and temporal reasoning.
📝 Tips for Applying to FutureSearch
Tailor your resume to highlight projects involving LLM evaluation, forecasting, or agentic systems—mention specific benchmarks or datasets you've created.
In your cover letter, explicitly connect your research to open problems in assessing LLM-generated information (e.g., factuality, uncertainty estimation).
Showcase any experience with temporal reasoning or time-series forecasting in AI, even if not directly LLM-related.
Prepare a short portfolio or GitHub repo with examples of evaluation pipelines or analysis of LLM performance.
Mention familiarity with FutureSearch's published work or blog posts (e.g., on forecasting tournaments or AI epistemics).
✉️ What to Emphasize in Your Cover Letter
['Your specific experience designing benchmarks or evaluation metrics for LLMs.', 'Why you are excited about the intersection of AI forecasting and epistemics.', "How your research approach aligns with FutureSearch's mission of reliable reasoning about the future.", "Concrete examples of how you've improved model evaluation or identified failure modes in LLMs."]
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Read FutureSearch's blog posts and any published papers on forecasting and LLM evaluation.
- → Familiarize yourself with the concept of 'AI epistemics' and current debates (e.g., calibration, confidence).
- → Study the company's open-source contributions or datasets (if any) related to forecasting.
- → Understand the landscape of LLM evaluation frameworks (e.g., BIG-Bench, HELM, AlpacaEval) and their limitations.
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Submitting a generic application that doesn't mention forecasting or epistemics—shows lack of research.
- Overemphasizing traditional ML without connecting to LLM-specific evaluation challenges.
- Ignoring the remote-first culture: failing to discuss how you collaborate and communicate asynchronously.
📅 Application Timeline
This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.
Typical hiring timeline:
Application Review
1-2 weeks
Initial Screening
Phone call or written assessment
Interviews
1-2 rounds, usually virtual
Offer
Congratulations!