Full-Stack Machine Learning Engineer
Role: Full-Time, Long-Term Required: Python Preferred: C++
OVERVIEW
We are seeking a versatile ML engineer to join as a core member of our technical team. This is a long-term position for someone who wants to build sophisticated production systems and grow with a small, focused team. You will work across the entire stack—from data ingestion and feature engineering through model training, validation, and deployment.
The ideal candidate combines strong software engineering fundamentals with deep ML expertise, particularly in time series forecasting and quantitative applications. You should be comfortable operating independently, making architectural decisions, and owning systems end-to-end.
CORE TECHNICAL REQUIREMENTS
Python (Required): Professional-level proficiency writing clean, production-grade code—not just notebooks. Deep understanding of NumPy, Pandas, and their performance characteristics. You know when to use vectorized operations, understand memory management for large datasets, and can profile and optimize bottlenecks. Experience with async programming and multiprocessing is valuable.
Machine Learning (Required): Hands-on experience building and deploying ML systems in production. This goes beyond training models—you understand the full lifecycle: data validation, feature engineering, model selection, hyperparameter optimization, validation strategies, monitoring, and maintenance.
Specific experience we value: gradient boosting frameworks (LightGBM, XGBoost, CatBoost), time series forecasting, probabilistic prediction and uncertainty quantification, feature selection and dimensionality reduction, cross-validation strategies for non-IID data, model calibration.
You should understand overfitting deeply—not just as a concept but as something you actively defend against through proper validation, regularization, and architectural choices.
Data Pipelines (Required): Design and implement robust pipelines handling real-world messiness: missing data, late arrivals, schema changes, upstream failures. You understand idempotency, exactly-once semantics, and backfill strategies. Experience with workflow orchestration (Airflow, Prefect, Dagster) expected. Comfortable with ETL/ELT patterns, incremental vs full recomputation, data quality monitoring, database design and query optimization (PostgreSQL preferred), time series data at scale.
C++ (Preferred): Experience valuable for performance-critical components. Writing efficient C++ and interfacing with Python (pybind11, Cython) is a significant advantage.
HIGHLY DESIRABLE: MULTI-AGENT ORCHESTRATION
We are building systems leveraging LLM-based automation. Experience with multi-agent frameworks highly desirable: LangChain, LangGraph, or similar agent frameworks; designing reliable AI pipelines with error handling and fallbacks; prompt engineering and output parsing; managing context and state across agent interactions. You do not need to be an expert, but genuine interest and hands-on experience will set you apart.
DOMAIN EXPERIENCE: FINANCIAL DATA AND CRYPTO
Preference for candidates with experience in quantitative finance, algorithmic trading, or fintech; cryptocurrency markets and their unique characteristics; financial time series data and forecasting systems; market microstructure, volatility, and regime dynamics. This helps you understand why reproducibility is non-negotiable, why validation must account for temporal structure, and why production reliability cannot be an afterthought.
ENGINEERING STANDARDS
Code Quality: Readable, maintainable code others can modify. Proper version control (meaningful commits, branches, code review). Testing where appropriate. Documentation: docstrings, READMEs, decision records.
Production Mindset: Think about failure modes before they happen. Build in observability: logging, metrics, alerting. Design for reproducibility—same inputs produce same outputs.
Systems Thinking: Consider component interactions, not just isolated behavior. Understand tradeoffs: speed vs accuracy, flexibility vs simplicity. Zoom between architecture and implementation.
WHAT WE ARE LOOKING FOR
Self-Direction: Given a problem and context, you break it down, identify the path forward, and execute. You ask questions when genuinely blocked, not when you could find the answer yourself.
Long-Term Orientation: You think in years, not months. You make decisions considering future maintainability.
Intellectual Honesty: You acknowledge uncertainty and distinguish between what you know versus guess. When something fails, you dig into why.
Communication: You explain complex concepts clearly and document your reasoning.
EDUCATION
University degree in a quantitative/technical field preferred: Computer Science, Mathematics, Statistics, Physics, Engineering. Equivalent demonstrated expertise through work also considered.
TO APPLY
Include: (1) CV/resume, (2) Brief description of a production ML system you built, (3) Links to relevant work if available, (4) Availability and timezone.
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