Quantitative ML Engineer

Quantitative Machine Learning Engineer

adaptive, agent-based ML systems

Key Responsibilities

• Design & Build Agentic ML Systems:

  Develop autonomous and semi-autonomous agents that perform data acquisition, alpha discovery, backtesting, and execution optimization.

• End-to-End ML Engineering:

  Architect, train, and deploy ML pipelines for HFT/MFT and wholesale trading, from signal generation to execution integration.

• Quantitative Research Integration:

  Collaborate with quant researchers to translate theoretical models into production-ready predictive and optimization systems.

• Market Forecasting:

  Develop deep learning, time series, and reinforcement learning models for price movement prediction and regime detection.

• Trading Optimization:

  Build reinforcement and meta-learning frameworks that adaptively tune strategy parameters in live environments.

• Scalable ML Infrastructure:

  Implement real-time inference, model versioning, and continuous learning pipelines for production systems.

• Performance Evaluation:

  Rigorously validate models with historical and synthetic simulations, ensuring robustness, latency, and financial soundness.

• Documentation & Collaboration:

  Maintain high standards of reproducibility, version control, and code documentation across research and deployment layers.

What You’ll Gain

• Work at the frontier of

  AI, quantitative finance, and agentic automation

  .
• Collaborate with

  quant researchers, data engineers, and trading teams

  shaping next-gen trading systems.
• Exposure to

  meta-optimization frameworks

  ,

  reinforcement learning

  , and

  multi-agent orchestration

  .
• Hands-on experience with

  low-latency ML deployment

  , GPU acceleration, and distributed training in real trading environments.
• Ownership of models that directly influence

  market-making, forecasting, and strategy execution

  .
• Continuous learning and experimentation in a

  research-first, innovation-driven

  environment.

Qualifications

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Applied Mathematics, Financial Engineering, or a related quantitative field.

• 3+ years

  of experience developing and deploying ML models in production (preferably in finance, trading, or large-scale decision systems).
• Strong proficiency in

  Python

  and ML frameworks: PyTorch, TensorFlow, scikit-learn, NumPy, Pandas.
• Deep understanding of

  supervised, unsupervised, and reinforcement learning

  ,

  time-series modeling

  , and

  probabilistic forecasting

  .
• Experience building

  scalable data pipelines

  with Kafka, Flink, or Ray and deploying models in Docker/Kubernetes environments.
• Knowledge of

  market microstructure

  ,

  portfolio optimization

  , or

  signal-based trading systems

  is highly desirable.
• Familiarity with

  meta-learning

  ,

  agent-based system design

  , or

  multi-agent coordination

  is a strong plus.
• Solid analytical, programming, and debugging skills with an emphasis on

  system reliability and latency optimization

  .

Preferred Technical Stack

• Languages:

  Python, C++, Rust

• ML Infrastructure:

  Ray, MLflow, Airflow, Weights & Biases

• Data Systems:

  Kafka, Redpanda, Redis, QuestDB

• Model Deployment:

  Triton Inference Server, TorchServe, or custom GPU inference

• Cloud/Hybrid Setup:

  Kubernetes, ArgoCD, Helm