Job description

Type: Full-time | Location: Remote | Function: Alignment & Explainability | Reports to: CTO/CEO About Elloe AI Elloe is building the immune system for AI. We help organizations in healthcare, finance, and policy deploy GenAI systems that are explainable, auditable, and safe. Our stack fuses SHAP explainability, secure Vault sync, and red-team simulation defense — all built by engineers from MIT and Harvard. About the Role: You’ll own the design of SHAP-driven explainability and model alignment systems for GenAI in high-stakes industries. From human-in-the-loop correction to production-grade dashboards, you’ll architect and ship the critical loops that make AI traceable, safe, and compliant. for real-world GenAI. This means SHAP-layer visualizations, alignment experiments with AutoHeal, and deeply integrating human feedback into every model correction. You’ll own core loops that power how Elloe learns to heal itself. What You’ll Own • Design and implement SHAP-based explanation layers for GenAI outputs • Architect AutoHeal interventions for misaligned model behavior • Build alignment experiments grounded in real-world compliance datasets • Own deployment of explainability dashboards for internal and customer usage Who You Are • MIT/Harvard (CS, EECS, HST) with strong research-to-production instincts • Familiar with SHAP, LIME, or other explainability libraries • Able to design alignment experiments that are both empirical and operational • Thrive in ambiguity and ship aligned features end-to-end Why Now GenAI is entering regulated sectors faster than governance can keep up. AutoHeal v4.2 is Elloe’s leap toward traceable, compliant AI — just as hospitals, banks, and policy orgs realize they need it. This is the right mission at the right moment. You’ll Leave This Role With • Founder-level ownership on compliance AI infra • A deep technical legacy in one of GenAI’s most urgent domains • Equity in a company built to define real-world alignment • A public portfolio of traceable, explainable AI systems Logistics & Application • Compensation: Salary + equity • Commitment: Full-time or intense part-time (15+ hrs/week) • Start: Rolling (ideal: within 2–4 weeks) • To Apply: Tell us how you'd extend SHAP to make multi-modal outputs traceable

Requirements

Must-haves

Type: Full-time | Location: Remote | Function: Alignment & Explainability | Reports to: CTO/CEO
What You’ll Own
Design and implement SHAP-based explanation layers for GenAI outputs
Architect AutoHeal interventions for misaligned model behavior
Build alignment experiments grounded in real-world compliance datasets
Own deployment of explainability dashboards for internal and customer usage
MIT/Harvard (CS, EECS, HST) with strong research-to-production instincts
Familiar with SHAP, LIME, or other explainability libraries
Able to design alignment experiments that are both empirical and operational
Thrive in ambiguity and ship aligned features end-to-end
You’ll Leave This Role With
Founder-level ownership on compliance AI infra
A deep technical legacy in one of GenAI’s most urgent domains
Equity in a company built to define real-world alignment
A public portfolio of traceable, explainable AI systems
Logistics & Application
Compensation: Salary + equity
Commitment: Full-time or intense part-time (15+ hrs/week)
Start: Rolling (ideal: within 2–4 weeks)
To Apply: Tell us how you'd extend SHAP to make multi-modal outputs traceable

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

Platform: gem
Effort: medium