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Technology Roles：Model Engineer June 25, 2026 utc Careers, Careers-EN

Model Engineer：Financial Time-Series Models & LLM Fine-Tuning 

Work Location 

Global 

 Employment Type 

Full-Time； Hybrid 

 Reporting To 

Chief Executive Officer (CEO) 
Compensation StructureBase Salary + Performance Bonus  Working Mode 

Day shift 

 Number of Vacancies 

Several 
Compensation 

To be discussed during the interview. 

 

    

 

▎ Role Mission:  

You will contribute to the firm’s quantitative modeling platform, including financial time-series modeling, market regime analysis, volatility forecasting, news-event impact research, and LLM fine-tuning, evaluation, inference services, and governance based on OpenAI API / enterprise-grade solutions. This role does not expose proprietary production strategy details, but you will work on research abstractions, data experiments, evaluation frameworks, and engineering implementation. 

▎ Responsibilities 

  1. Research financial time-series models, including hidden-state models, state-space models, volatility models, sequence classification, anomaly detection, and multi-timeframe market regime analysis. 
  2. Help build internal model experimentation frameworks, including feature engineering, training sample generation, labeling, cross-validation, walk-forward testing, and backtest evaluation. 
  3. Study relationships among news events, macroeconomic indicators, market prices, and trading behavior to build event-driven research datasets. 
  4. Work on OpenAI model fine-tuning workflows, including dataset construction, JSONL sample standards, prompt design, model evaluation, output contract validation, and inference service integration. 
  5. Design structured model outputs that can be reliably consumed by trading automation, risk systems, and trading desk monitoring tools. 
  6. Contribute to model evaluations, including directional accuracy, regime stability, confidence calibration, latency, error rate, replay testing, and edge-case analysis. 
  7. Assist in model governance, including model versioning, training data versioning, evaluation reports, deployment approval, rollback mechanisms, and audit logs. 
  8. Collaborate with data engineering teams to transform historical market data, news data, and macro data into trainable, evaluable, and inference-ready datasets. 

▎ Requirements 

  1. Graduated senior, Master’s, or PhD student in Computer Science, AI, Machine Learning, Statistics, Mathematics, Financial Engineering, Physics, Electrical Engineering, or related fields. 
  2. Strong Python skills with experience in at least one of NumPy, pandas, scikit-learn, PyTorch, JAX, or TensorFlow. 
  3. Understanding of machine learning fundamentals, including supervised learning, time series, classification, regression, probabilistic models, overfitting, cross-validation, and model evaluation. 
  4. Interest in financial markets, quantitative trading, market regime analysis, volatility forecasting, or news-event analytics. 
  5. Familiarity with LLM concepts such as prompt engineering, fine-tuning, RAG, function calling / structured outputs, Evals, and model monitoring is a plus. 
  6. Familiarity with OpenAI API, fine-tuning, enterprise data governance, security, and permission controls is a plus. 
  7. Strong experimental discipline, including tracking experiment configs, data versions, model versions, and evaluation results. 
  8. Trading experience is not required, but sensitivity to uncertainty, risk, sample bias, and model failure is important. 

▎ Nice to Have 

  • Experience with HMMs, state-space models, Kalman Filters, Bayesian modeling, GARCH, HAR-RV, or Transformer-based time-series models. 
  • Experience in market direction prediction, volatility forecasting, event studies, or quantitative factor research. 
  • Experience with OpenAI fine-tuning, Evals, structured outputs, JSON schema, or model API services. 
  • Experience with MLflow, Weights & Biases, DVC, Hydra, Ray, SageMaker, Docker, or Kubernetes. 
  • Experience in paper reproduction, Kaggle, mathematical modeling contests, quant competitions, or open-source projects. 

▎ Common Requirements 

  1. Strong interest in fintech, quantitative trading, AI, data engineering, or automation systems.  
  2. Strong learning ability, engineering discipline, and documentation habits.  
  3. Ability to follow confidentiality rules, data access policies, and code security standards.  
  4. Comfortable collaborating across data, modeling, trading automation, trading desk, and operations teams.  
  5. Live trading experience is not required; we value fundamentals, engineering quality, learning speed, and responsibility. 

※Internship is welcomed！ 

▎Application Method 

Please send your resume (with photo) and scanned copies of relevant certificates to the recruitment email：career@utc.group.  

The email subject format shall be：  

「Position Applied – Intended Work City – Full Name」 

 

▎ Interview Process 

  • Initial Screening Interview (Online/Phone): Assess the candidate’s professional background and overall fit for the role.  
  • Practical Assessment: Complete job-related tasks in a simulated working environment to evaluate practical skills and execution.  
  • Final Interview: Meet with the Chief Executive Officer (CEO) and relevant department leaders. 

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