Machine Learning Engineer

Responsibilities

• Design and build the overall architecture for a multi-model ML system that integrates distinct models (e.g., media prediction, bioprocess optimization, sensory profile, GEM-based outputs) into a unified decision pipeline
• Develop robust interfaces between sub-models to enable modularity, information flow, and cross-validation across stages (e.g., outputs of one model feeding into another)
• Implement model orchestration logic to allow conditional routing, fallback mechanisms, and ensemble strategies across different models
• Build and maintain pipelines for training, testing, and deploying multiple models across different data domains
• Optimize inference efficiency and reproducibility by designing clean APIs and containerized deployments
• Translate conceptual product flow into technical architecture diagrams, integration roadmaps, and modular codebases
• Implement model monitoring and versioning infrastructure to track performance drift, flag outliers, and allow comparison across iterations
• Collaborate with data engineers and researchers to abstract away biological complexity and ensure a smooth ML-only engineering focus
• Lead efforts to refactor and scale ML infrastructure for future integrations (e.g., generative layers, reinforcement learning modules)

Qualifications

• Bachelor s or Master s degree in Computer Science, Machine Learning, Computational Biology, Data Science, or a related field
• Proven experience developing and deploying multi-model machine learning systems in a scientific or numerical domain
• Exposure to hybrid modeling approaches and/or reinforcement learning strategies

• Experience with multi-model systems
• Worked with numerical/scientific datasets (multi-modal datasets)
• Hybrid modelling and/or RL (AI systems)

Core technical skills

• Machine Learning Frameworks: PyTorch, TensorFlow, scikit-learn, XGBoost, CatBoost
• Model Orchestration: MLflow, Prefect, Airflow
• Multi-model Systems: Ensemble learning, model stacking, conditional pipelines
• Reinforcement Learning: RLlib, Stable-Baselines3
• Optimization Libraries: Optuna, Hyperopt, GPyOpt
• Numerical & Scientific Computing: NumPy, SciPy, panda
• Containerization & Deployment: Docker, FastAPI
• Workflow Management: Snakemake, Nextflow
• ETL & Data Pipelines: pandas pipelines, PySpark
• Data Versioning: Git
• API Design for modular ML blocks