Machine Learning Engineer – VC-Backed Digital Document Startup (via CodeRound AI)

About the Role & Client Startup

Key Responsibilities

• Document AI & Extraction Pipelines: Develop, evaluate, and deploy machine learning models for document understanding, classification, key-value extraction, and automated workflow processing.
• Unstructured Data Preprocessing: Perform data cleaning, feature engineering, synthetic data generation, and tokenization on complex structured and unstructured datasets.
• Modern NLP & LLM Integration: Experiment with modern NLP architectures, Large Language Models (LLMs), and Generative AI techniques to optimize document processing accuracy and field matching.
• Production Model Deployment & MLOps: Integrate ML inference endpoints into backend application services, ensuring low latency, high throughput, and system reliability.
• Model Lifecycle & Monitoring: Continuously monitor model drift, accuracy metrics, and latency performance in production, iterating on retraining pipelines as new edge cases arise.

Required Qualifications & Competencies

• Experience: 2+ years of hands-on industry experience building and deploying machine learning or applied AI models.
• Core Machine Learning: Solid foundation in core ML algorithms, statistical modelling, model training, evaluation metrics, and hyperparameter tuning.
• Programming & Frameworks: High proficiency in Python alongside standard frameworks like PyTorch, TensorFlow, or Scikit-Learn.
• Data Engineering & Preprocessing: Demonstrated experience working with messy, large-scale unstructured or structured datasets.
• Startup Mindset: Ability to take complete ownership of critical AI capabilities in a fast-paced environment.

Role Summary