Lead integration of Cellcraft IQ 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 directly with customer technical teams to scope, configure, and validate deployments
Collaborate closely with Cellcraft's senior integration engineer (UK-based) throughout each deployment
Troubleshoot integration issues across diverse bioreactor OEM environments
Document configurations, integration steps, and site-specific setups for each customer
Act as the primary technical point of contact for customers during and after onboarding
Feed field observations and customer feedback back to the product and engineering team
May also include travel to customer facilities as required for on-site commissioning and support

Strong Python programming : NumPy, Pandas, SciPy, scikit-learn (production code, not just notebooks)
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 communication standards
Understanding of feedback control in dynamic systems
Experience working with noisy sensor data and signal filtering techniques
Ability to interpret process dynamics from time-series data (lag, dead time, oscillations)
Degree in Control Engineering, Computer Science, Chemical/Bioprocess Engineering, or related quantitative field
Must have existing right to work in Singapore
Fluent in English

Equity participation with the potential to share in the company’s long term success
Opportunity to work on real world physical AI systems applied to biomanufacturing
Build algorithms that move from experimental data to production scale systems
Exposure to a fast growing field at the intersection of machine learning, biology and industrial automation
Opportunity for significant responsibility and rapid career progression as the technology scales
Contribute to technologies enabling sustainable food and medicine production
Backed by the University of Cambridge Judge Business School and leading international venture capital investors
Potential for a full time role and competitive salary following successful completion of the project

Cellcraft

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