Fellowship Title: AI Engineer Fellow
Department: Engineering & LunarTech Labs
Location: Remote (Global) / Hybrid (depending on specific team hub)
Type: Fellowship
Why This Fellowship Exists
The gap between learning AI and doing AI in production is enormous. Most graduates can train a model in a notebook—but few know how to deploy it reliably, monitor it in production, and iterate on it when the real world doesn't match the training data.
The LUNARTECH AI Engineering Fellowship was created to close that gap. We take ambitious, technically sharp individuals and immerse them in the full reality of enterprise AI—from data pipelines to deployed models, from research prototypes to systems serving thousands of users.
About LUNARTECH
LUNARTECH operates at the frontier of AI education and enterprise AI delivery. LunarTech Academy is our platform for democratizing tech education. LunarTech Labs is where we engineer production AI systems for clients in healthcare, heavy industry, and telecommunications. We are builders, not theorists. Every system we design must work in the real world.
What the Fellowship Looks Like
You will join a cross-functional AI engineering team and contribute to active projects from day one. There is no "onboarding limbo"—you will have a mentor, a codebase, and a problem to solve within your first week.
Over the course of the fellowship, you will:
Work on model development—designing, training, and evaluating ML and deep learning models for NLP, computer vision, and predictive analytics use cases.
Build production ML systems—constructing end-to-end pipelines that move models from experimentation to reliable deployment, including data ingestion, feature stores, model serving, and monitoring.
Engage with LLM engineering—developing applications using Large Language Models, including RAG architectures, prompt optimization, fine-tuning, and agentic frameworks.
Drive model optimization—improving inference speed, reducing memory footprint, and ensuring cost-effective scaling for enterprise workloads.
Champion responsible AI—implementing explainability techniques and bias audits, especially for high-stakes domains like healthcare where trust is non-negotiable.
Collaborate across teams—working with Data Engineers, DevOps, and Product Managers to deliver integrated solutions and translate business needs into technical plans.
The Ideal Candidate
You don't need years of industry experience. You need a demonstrated ability to learn fast, think critically, and write code that works.
- Strong ML fundamentals through academic study, research, bootcamps, or self-directed projects
- Proficiency in Python and hands-on experience with PyTorch or TensorFlow
- Exposure to LLMs and modern NLP—whether through coursework, Kaggle, personal builds, or research
- Solid mathematical foundations in statistics, linear algebra, and optimization
- Software engineering discipline—you write tested, readable code and use Git fluently
- Strong communication skills—you can explain your approach clearly and ask good questions
Bonus Points
- Experience with ML infrastructure (MLflow, Weights & Biases, Kubeflow)
- Familiarity with containerization (Docker) and cloud environments (AWS/Azure)
- Published work or open-source ML contributions
- Domain knowledge in healthcare, energy, or telecommunications
- Experience with multi-modal systems, reinforcement learning, or edge deployment
What LUNARTECH Offers You
Impact: Your work ships. You will see your models serve real users in critical industries—not gather dust in a research repo.
Mentorship That Matters: You will be paired with a senior AI engineer who will guide your development through structured check-ins, code reviews, and design sessions.
Learning at Scale: Full, unlimited access to LunarTech Academy—our comprehensive catalog of courses in AI, data science, cloud, and software engineering.
A Culture of Excellence: We are remote-first, globally distributed, and fiercely committed to integrity. We don't micromanage. We set high standards and trust you to rise to them.
Fellowship Stipend: A competitive stipend for the duration of the fellowship, with a transparent pathway to full-time employment based on your performance and contributions.
Type: Fellowship
Work Location: Remote / Hybrid
Job Types: Full-time, Internship
Work Location: Remote
