Planned hiring
Applied Scientist, Behavioral AI
Lucy is a proactive AI coach from Lucy Labs that lives on your desktop as a cloud-backed app.
Planned hiring. This description explains the work we are preparing to hire for; it is not a confirmed start date or offer.
Help Lucy distinguish plausible tasks and goals, ask for clarification when useful and stay quiet when the evidence is weak. You will develop methods that turn incomplete observations of work into useful, correctable interpretations. The scientific challenge is to recognize what someone may be trying to accomplish without pretending that an observed action reveals their intention.
The work
Help Lucy distinguish plausible tasks and goals, ask for clarification when useful and stay quiet when the evidence is weak. You will develop methods that turn incomplete observations of work into useful, correctable interpretations. The scientific challenge is to recognize what someone may be trying to accomplish without pretending that an observed action reveals their intention.
Someone researching a company might be preparing for a meeting, assessing a supplier or simply learning. You will help Lucy distinguish plausible tasks and goals, ask for clarification when useful and stay quiet when the evidence is weak. The aim is better coaching, with methods that can be evaluated and maintained.
What you will own
- Develop and compare methods for Action-to-Task-to-Goal inference using contextual and temporal evidence. Start with inspectable rules and baselines, then justify more complex statistical or model-assisted methods through measured improvement.
- Investigate recurring work patterns and rank potential workflow or solution opportunities. Separate an observed sequence, a candidate interpretation and a user-confirmed goal in both experiments and output contracts.
- Define confidence, calibration, abstention and correction behavior. Study when more information improves a recommendation and when it adds cost, delay or intrusion without useful evidence.
- Build reproducible experiments, error analyses and prototypes that production engineers can implement. Document data assumptions, relevant slices, latency or compute tradeoffs and conditions where a method should not be used.
- Connect methods to the Knowledge Graph's tasks, tools, skills and prerequisites. Use research about actual business processes to test the existing work concepts and surface gaps to Product.
- Examine drift, cold starts, ambiguous vocabulary and feedback bias. Work within permission, minimization and retention requirements; incomplete observations are a property of the problem to handle, not a reason to bypass user controls.
Your partners
AI Engineering productionizes and operates inference. Knowledge Graph Engineering owns semantic implementation; the Product Manager owns prioritization and acceptance of product meaning. AI Evaluation provides independent datasets and quality evidence, so a better development score alone cannot establish release readiness. Behavioral Science studies how interventions affect learning and habits; Product Data Science measures customer outcomes. Your distinctive responsibility is the inference and recommendation method, its uncertainty and its limitations.
What you bring
You have applied quantitative methods to noisy event, sequence, language or behavioral data and can explain both a successful model and a useful failed experiment. Strong Python, statistics and reproducible experimentation matter. Experience with ranking, probabilistic reasoning, information retrieval or interpretable machine learning is helpful. We value evidence of translating research into a working product and collaborating on independently evaluated results more than a prescribed degree or publication count.
Your first 90 days
Select one narrow inference problem with Product, inspect available research and data, and reproduce or establish its simplest credible baseline. Build an error taxonomy with AI Evaluation. Improve one material failure mode, compare against independent examples and document calibration and abstention behavior. Hand production engineering a reproducible prototype and explicit operating constraints, including what remains unproven.
Next step
Express your interest
Email people@lucylabs.ai with the role title, a short introduction and relevant CV or portfolio. This is planned hiring; we will confirm current availability and the individual working arrangement.
Lucy Labs confirms the working arrangement, employer, location, eligibility, compensation and terms for each role. Our mailing address does not determine the job location or guarantee remote employment, worldwide eligibility or visa sponsorship.
Do not send passwords, government identifiers or bank details. Read the application privacy notice.
Frequently asked questions
Is this role open at Lucy Labs?
Lucy Labs lists this role as planned hiring. Availability depends on the need, team readiness and individual arrangement; it does not promise an immediate vacancy or start date.
Where does this work take place at Lucy Labs?
Lucy Labs confirms the working arrangement, employer, location, eligibility, compensation and terms for each role. Our mailing address does not determine the job location or guarantee remote employment, worldwide eligibility or visa sponsorship.
What does this role own at Lucy Labs?
Lucy Labs: Help Lucy distinguish plausible tasks and goals, ask for clarification when useful and stay quiet when the evidence is weak. You will develop methods that turn incomplete observations of work into useful, correctable interpretations. The scientific challenge is to recognize what someone may be trying to accomplish without pretending that an observed action reveals their intention.
How do I express interest in Lucy Labs?
Email people@lucylabs.ai with the role title, a short introduction and relevant CV or portfolio. Lucy Labs uses the application to assess and respond to it; retention for future opportunities is a separate explained purpose and choice.
