Forward Deployed Staff - Education

Levelup Labs; United States; Teletrabalho; USD 120 000 - 180 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Remote-first

Async-friendly

Growth opportunities

Early team member

Resumo da oferta

LevelUp Labs seeks a Forward Deployed Staff - Education to design, build, and teach the next generation of production AI systems. You’ll work across engineering, training, and content, partnering with client teams to deliver production-grade solutions and curriculum.

In this role you’ll implement LLM-powered apps, develop educational content, and transfer knowledge to client engineers. Remote-first with flexible schedules, you’ll influence education as well as code.

Qualificações

• Must have engineering experience building production software (2+ years).
• Strong programming skills in Python; experience with TS/JS, Go, or Rust is a plus.
• Deep experience with AI/ML systems: LLMs, RAG, agents, evaluations.
• Strong software engineering practices: testing, CI/CD, observability, docs.
• Experience with cloud platforms (AWS, GCP, or Azure).
• Production mindset; shipped systems handling real traffic and users.

Responsabilidades

• Build production-grade AI systems alongside client engineering teams.
• Build LLM-powered applications: RAG systems, agents, evaluation frameworks, etc.

Conhecimentos

Python

TypeScript/JavaScript

Go or Rust

AI/ML systems

CI/CD

Observability

Documentation

Communication

Ferramentas

AWS

GCP

Azure

Descrição da oferta de emprego

The Role As Forward Deployed Staff - Education

The Role As Forward Deployed Staff - Education, you'll work across the full spectrum of what we do: building production AI systems, delivering technical training, and creating educational content. This is not a siloed role—you'll move fluidly between implementation, teaching, and content creation based on what's needed. The "Forward Deployed" Philosophy: Everyone at LevelUp Labs is a generalist. You'll be expected to contribute across engineering, training, content, and client work. However, this role has a spike in education and curriculum development —you're someone who loves teaching and creating learning experiences, while still being a strong engineer. What "spike" means: You can do all three, but your edge is in making complex things learnable. You're the person who builds something and immediately thinks about how to teach it to others.

What You'll Do

• Build Design and implement production-grade AI systems alongside client engineering teams
• Build LLM-powered applications: RAG systems, agents, evaluation frameworks, etc.
• Own technical architecture decisions and trade-offs
• Write code that’s maintainable, tested, documented, and built to last
• Debug complex issues across the stack
• Embed Work directly with client engineering teams as a peer, not an outside consultant
• Understand client constraints, existing systems, and organizational context
• Communicate progress and challenges to both technical and non-technical stakeholders
• Transfer knowledge to client teams—leave them better than you found them
• Learn & Share
• Distill learnings from implementations into patterns we can reuse
• Contribute to our courses, documentation, and internal tooling
• Stay current with AI developments—evaluate what actually works in production
• Participate in technical discussions and code reviews

What We're Looking For

• Must Have Engineering 2+ years building production software systems
• Strong programming skills (Python required; experience with TypeScript/JavaScript, Go, or Rust a plus)
• Deep experience with AI/ML systems: LLMs, RAG, agents, fine-tuning, evaluations
• Strong understanding of software engineering best practices (testing, CI/CD, observability, documentation)
• Experience with cloud platforms (AWS, GCP, or Azure)
• Production Mindset
• You’ve shipped systems that handle real traffic and real users
• You think about failure modes, edge cases, and operational concerns
• You know the difference between demo code and production code
• You’ve been paged at 2am and fixed something that was broken
• Communication
• Can explain technical decisions to non-technical stakeholders
• Comfortable presenting architecture and progress to client leadership
• Clear written communication (documentation, design docs, async updates)
• Can work effectively with client teams across different cultures and timezones
• Mindset
• Self-directed—you don’t need someone telling you what to do next
• Comfortable with ambiguity and rapidly changing requirements
• Ego-free: you’ll do whatever needs doing to ship
• Strong opinions, loosely held
• Nice to Have
• Experience with enterprise clients (understanding their constraints and pace)
• Prior consulting or client-facing engineering experience
• Contributions to open source projects
• Background with observability and evaluation frameworks for AI
• Experience leading technical projects or mentoring engineers

What You’ll Get

• Competitive compensation (base + performance bonuses + outcome-based bonus per engagement)
• Work on challenging problems with leading companies
• Learn from a team with 30+ enterprise implementations and published AI research
• Flexibility: remote-first, async-friendly
• Direct impact: you’re building real systems, not maintaining legacy code
• Growth: as an early team member, you’ll shape our engineering culture

Originally posted on Himalayas