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Imubit is seeking an Implementation Engineer to build and deploy AI-driven optimization models for live refining and petrochemical plants. You will own projects from scoping through closed-loop commissioning, working directly with customer teams to identify optimization opportunities, build machine learning models of complex processes, and train operators and engineers to run them in production.
Key responsibilities include:
- Scoping optimization opportunities with customers: defining objectives, constraints, and value cases
- Leading the technical side of projects: planning modeling work, maintaining schedules, and owning the technical relationship with customer engineers
- Analyzing plant time-series data to establish feasibility and quantify data quality
- Building and evaluating AI models (soft sensors, process response models, closed-loop optimizers)
- Validating learned relationships against first principles and defending modeling decisions in design reviews
- Commissioning models on site, training operators and engineers, and monitoring closed-loop performance through sign-off
- Troubleshooting deployed models post-launch, retraining around turnarounds and process changes
- Learning systems integration: plant data connections, tag configuration, control system integration
- Building automations, documentation, and training to enable customers to build and monitor their own models
- Channeling field insights back to Product and R&D teams to shape the platform roadmap
You will work directly with Imubit's founders in a collaborative, innovative environment. The role combines deep process engineering expertise with machine learning application in a customer-facing context. Travel to client sites is required (up to 25%).
Minimum Qualifications:
- 4+ years of engineering experience in refining, petrochemicals, chemicals, NGL, or another continuous-process industry
- BSc in Chemical Engineering, related technical discipline, or equivalent practical experience (advanced degrees a plus)
- Process engineering or process control background with working knowledge of APC and optimization concepts (gains, constraints, objective functions)
- Hands-on analysis of plant time-series data, including visualization and sound conclusion-drawing
- Client-facing or operations-support experience sufficient to earn credibility with plant engineers and operators
- Willingness to travel up to 25%
- Strong communication and presentation skills
Preferred Qualifications:
- Operating plant experience as unit process engineer, control engineer, or in operations support
- Planning and economics (P&E) experience, including building or running refinery LP models
- APC or real-time optimization experience
- Record of leading projects or implementations with technical client relationships
- Scripting or data science experience (e.g., Python)
- Exposure to plant data systems (DCS, process historians, OPC)
- Consulting or customer-facing vendor experience serving industrial clients
- Familiarity with AI, machine learning, or advanced analytics applied to industrial processes
- Record of improving plant performance with minimal capital spend
