What are the responsibilities and job description for the Applied Scientist position at FalconFirst AI?
Company Description
FalconFirst is a decision intelligence company building an AI-native decision layer for product teams. Instead of shipping features based on gut feel and noisy opinions, we help teams validate ideas inside real workflows before development - using prototype interactions, user feedback and experiment signals to produce decision-grade outputs.
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Role Description
If you want predictable hours and a slow pace, don’t apply.
If you want to build the “brain” of a new product category and ship weekly, this is for you.
You’ll help build the intelligence layer behind Falcon Terminal: systems that convert messy behavioral signals unstructured feedback into Decision Cards; clear claims with evidence, uncertainty and recommended next tests. You’ll work across inference, agent workflows, evaluation/guardrails and instrumentation foundations.
This is not a “model in a notebook” role. You’ll ship production systems, iterate weekly and influence core product direction.
How we work: fast cycles, direct feedback, high ownership. Not a 9–5.
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Responsibilities (high-level)
- Turn prototype interactions feedback into structured evidence and decision-ready insights
- Build inference that quantifies uncertainty and improves as evidence accumulates
- Design lightweight NLP pipelines (clustering/embeddings) and use LLMs pragmatically (with evaluation)
- Build evaluation guardrails so outputs are grounded, consistent and reliable
- Collaborate with engineering on instrumentation/SDK primitives and workflow orchestration
- Help define what “good” looks like (quality metrics, stability checks, regressions)
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Qualifications
Must-have
- Strong fundamentals in statistics/inference (Bayesian experimental design is a plus)
- Strong Python (SQL is a plus; R/Stan/PyMC helpful)
- Practical experience with NLP (embeddings, clustering, summarization) and an evaluation mindset
- High agency: you move from ambiguity → shipped system quickly
- Ability to communicate clearly under uncertainty (PM-friendly explanations)
Nice-to-have
- Fine-tuning/distillation or prompt eval pipelines
- Product analytics experience (funnels, cohorts, paths)
- Reliability patterns for LLM/agent workflows (routing, fallbacks, caching, monitoring)