5 часов назад
Fullstack Developer (AI)
135 000 - 280 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Fullstack Developer (AI): Building and optimizing workforce management and scheduling platforms for customer support teams with an accent on algorithmic complexity and product design. Focus on solving NP-hard scheduling problems, building intuitive interfaces for complex data, and integrating AI-driven forecasting tools.
Location: On-site in San Francisco, CA or New York City, NY
Salary: $135,000 – $280,000
Company
is a unified platform that orchestrates human agents and AI at scale, helping companies like Canva, Etsy, and Robinhood optimize their support operations.
What you will do
- Own end-to-end product features from data modeling and API design to polished user interfaces.
- Rebuild schedule editing experiences to support thousands of agents with automated optimization.
- Translate complex real-world constraints, such as labor laws and union rules, into logic for solvers.
- Ship forecast interfaces that provide actionable insights for staffing decisions.
- Ensure high-performance schedule generation for large-scale enterprise customers.
Requirements
- Proven track record of shipping high-quality products and explaining technical decision-making.
- Strong generalist skills with the ability to work across the entire stack.
- Experience solving ambiguous, complex technical or organizational problems.
- Attention to detail in both code and user experience.
- Must be authorized to work in the United States (E-Verify participation).
Nice to have
- Familiarity with scheduling algorithms, operations research, or constraint solvers.
- Background in ML, statistical modeling, or forecasting.
- Experience in fields like support, operations, math, or physics that provides user empathy.
Culture & Benefits
- Comprehensive medical and financial benefits package.
- Stock options included in compensation.
- Focus on building an inclusive workplace with diverse perspectives.
- Opportunity to solve genuinely hard, NP-hard scale problems in a product-focused environment.
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