Quant Research AI Engineer

Quantitative Research & AI Engineering Internship

Company: Crypt0nest.io
Location: Remote (U.S. preferred but not required)
Duration: 15–20 weeks (flexible start)
Time Commitment: 10–20 hours/week
Compensation: Unpaid (for-credit or project certificate available)

About Us

Crypt0nest.io is an early-stage AI-driven investment intelligence platform building next generation predictive systems for digital assets. Our work integrates machine learning, quantitative finance, factor research, and agentic LLM systems to generate actionable insights and automated portfolio tools.

We operate under Tekly Studio—an innovation lab focused on real-world AI application, rapid prototyping, and product development. Our 2025 Summer Internship, we onboarded 30+ Interns and attracted 300+ applicants globally and are looking to onboard a select few for a Winter/Spring internship. Our Summer Interns gained skills required to secure roles at hedge funds, trading firms, and high-growth startups after gaining direct experience with our models, pipelines, and AI systems. 

This internship is designed for high-curiosity, high-ownership individuals who want to work directly inside a startup building real ML systems—not toy projects.

What You’ll Do

You will work across quant research, predictive modeling, and LLM-based agentic systems, supporting our core investment prediction engine and helping build the foundations for our upcoming MVP. You will collaborate with engineers across machine learning, backend, and data engineering to ship real features used in forecasting, anomaly detection, and automated reasoning. 

This role is ideal for students or early-career technologists pursuing careers in quantitative research, ML for finance, trading automation, or AI systems design.

Key Responsibilities

Quantitative & Trading Research

• Develop, test, and evaluate systematic crypto trading signals.
• Conduct time-series, factor modeling, and cross-sectional analysis.
• Build and refine backtesting pipelines.
• Compute performance metrics including IC, Sharpe, drawdowns, Sortino, and alpha contribution.

Machine Learning & Deep Learning

• Build predictive models using tree-based methods, deep learning, and hybrid ML architectures.
• Engineer features across price, volatility, social sentiment, on-chain datasets, and macro factors.
• Optimize models through walk-forward testing, hyperparameter tuning, and validation.

LLMs & Agentic AI

• Contribute to LLM-powered forecasting, reasoning, and portfolio-decision agents.
• Build or enhance multi-agent workflows using modern agent frameworks and tool augmented LLMs.
• Explore RAG pipelines, vector stores, and chain-of-thought reasoning for financial intelligence. 

Backend & Infrastructure

• Support FastAPI-based microservices for real-time model inference.
• Assist with MCP (Model Context Protocol) server development for multi-agent orchestration.
• Work with distributed compute, message queues, and pipeline automation as needed.

Required Skills

Technical

• Strong Python skills (NumPy, Pandas, Scikit-learn)
• Working knowledge of ML modeling and modern DL frameworks (PyTorch / TensorFlow).
• Understanding of quantitative finance (returns, risk metrics, factors, regime analysis).
• Ability to run backtests or simulations
• Familiarity with LLMs, RAG, or agent frameworks.

Soft Skills

• Clear communication and documentation
• Ability to work independently in ambiguous, high-ownership environments.
• Strong problem-solving mindset and curiosity.

Preferred Skills (Not Required)

• Experience with reinforcement learning for markets.
• Exposure to DeFi, on-chain data, or crypto analytics.
• Knowledge of vector databases (FAISS, Pinecone), OpenAI function-calling, or MCP.
• Experience in a quant club, trading competition, Kaggle, or open-source ML work.

What You’ll Gain

• Direct experience building real quant models used in an upcoming investment product.
• Exposure to multi-agent LLM systems, a fast-growing frontier in applied AI.
• Mentorship from engineers and researchers working on production-grade pipelines.
• End-of-program deliverables you can showcase:
  • A quant research report
  • A predictive ML model or agent-based system
  • A documented contribution to our codebase or datasets
• Strong recommendation letters and priority consideration for future paid roles.

How to Apply

Submit the following via our application form:
📎 Resume or LinkedIn profile
🔗 GitHub or portfolio (if available)
✍️ A short note (100–200 words) on what you hope to learn this summer

Apply here: https://forms.gle/kmh6S7VCxjgtBsuw7

Interview here: https://talent.flowmingo.ai/interview/04880d6f-f60b-433a-8ed8-d37fee404c65

Questions? Reach out after submitting the form.