Machine Learning Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop machine-learning models for applications in investment research and trading.

• Transform research concepts and raw data into models, experiments, and actionable insights.

• Oversee the complete process from data and model development through infrastructure, assessment, and documentation.

• Construct, train, refine, and enhance deep-learning models using market data.

• Establish and manage a small GPU environment utilizing a local machine or AWS instances.

• Supervise environments, drivers, containers, storage, experiment tracking, monitoring, and cost management.

• Conduct leakage-proof validation on time-ordered datasets and regime-aware testing.

• Create reliable baselines and differentiate authentic results from noise.

• Analyze, replicate, and assess recent research in foundation models, time series, and reinforcement learning.

• Compose succinct reports detailing experiments, results, significance, and subsequent steps.

• Leverage modern AI tools to expedite coding, literature review, and data manipulation while ensuring output accuracy.

• Collaborate directly with traders and researchers whose decisions are influenced by the models.

• Progress from managing a single project end-to-end to taking on broader research responsibilities.

⛳️ Requirements

• Early-career machine learning profile with proven fundamentals and development capability.

• Trading experience is not necessary.

• Solid understanding of optimization, initialization, normalization, attention mechanisms, model divergence, and plateaus.

• Familiarity with linear algebra, probability, and statistics.

• Capability to derive gradients of loss functions and analyze batch-size variations.

• Demonstrated evidence of independently developed projects, hackathon contributions, trained models, or utilized repositories.

• Proficient in idiomatic Python and PyTorch.

• Experience with NumPy and pandas.

• Ability to establish and operate small-scale GPU infrastructure either locally or on AWS.

• Knowledge of CUDA, containers, storage, monitoring, and cost management.

• Experience with time-ordered data, leakage-free splits, backtesting standards, and distribution shifts.

• Ability to replicate research papers and assess the performance of methods on the candidate's own data.

• Proficiency with contemporary AI tools for coding, literature, and data tasks.

• Strong written communication skills and the ability to produce clear research documentation.

• Capability to work independently in a remote, low-guardrail environment.

• Skills in fine-tuning or serving large language models, CUDA or Triton, and time-series forecasting are advantageous, but not mandatory.

🏝️ Benefits

• Bonus opportunities.

• Direct involvement with traders and researchers in an active trading environment.

• Well-funded firm with a unique approach to the markets.

• Intentional growth path with increasing responsibilities in research.

• Fully remote team structure.