Job Overview
Lead the integration of AI-driven platforms for biomanufacturing, focusing on connecting machine learning models to bioreactor systems for adaptive optimization, predictive monitoring, and autonomous process tuning. Transform bioprocessing into a data-driven discipline supporting scalable production in biopharma and cultivated meat.
Responsibilities
- Lead integration of AI platforms at customer sites, connecting to bioreactor systems via Modbus TCP, OPC UA, and PROFINET.
- Design data pipelines for acquisition, cleaning, and structuring of high-frequency sensor and process data.
- Integrate ML models with embedded controllers and supervisory control systems.
- Contribute to software architecture for scalable deployment of control and analytics systems.
- Work with customer technical teams to scope, configure, and validate deployments.
- Collaborate with senior integration engineer throughout deployments.
- Troubleshoot integration issues across diverse bioreactor environments.
- Document configurations, integration steps, and site-specific setups.
- Act as primary technical point of contact for customers during and after onboarding.
- Feed field observations and customer feedback to product and engineering teams.
- May include travel to customer facilities for on-site commissioning and support.
Qualifications
- Strong Python programming: NumPy, Pandas, SciPy, scikit-learn (production code).
- Control systems fundamentals: State-space representation, PID tuning, stability analysis.
- Time-series methods: Kalman filtering, ARIMA, or recurrent neural networks (LSTM/GRU).
- Real-time systems experience: Low-latency data processing, asynchronous programming, embedded constraints.
- Industrial protocols: Modbus, OPC-UA, MQTT, or similar SCADA/IoT standards.
- Domain knowledge: Understanding of feedback control in dynamic systems, noisy sensor data, signal filtering, interpreting process dynamics from time-series data.
- Degree in Control Engineering, Computer Science, Chemical/Bioprocess Engineering, or related quantitative field.
- Must have existing right to work in Singapore and be fluent in English.
Desirable Skills
- Bioprocess systems: Work with bioreactors, fermentation, or cell culture monitoring.
- Model Predictive Control (MPC) implementation experience (cvxpy, CasADi, MATLAB MPC Toolbox).
- Reinforcement learning for control (DQN, PPO, SAC).
- Physics-informed ML: Incorporating mechanistic models (ODEs) into neural network architectures.
- Edge ML deployment: TensorFlow Lite, ONNX Runtime, or custom C++ inference.
- Signal processing: FFT, wavelet transforms, spectral analysis for biosensors.
- Linux system administration: Docker, systemd services, remote deployment pipelines.
