Subject Matter Expert - ESP & Artifical Lift (Remote) at nybl

Dubai, Dubai, United Arab Emirates -

Full Time

Start Date

Immediate

Expiry Date

27 Dec, 25

Salary

0.0

Posted On

28 Sep, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Electrical Submersible Pump, ESP Systems, Machine Learning, Data Science, Diagnostics, Petroleum Production, Artificial Lift, Multiphase Flow, Collaboration, Analytical Mindset, Communication Skills, Mentoring, Product Strategy, Optimization, Anomaly Detection, Engineering

Industry

Information Technology & Services

Description

About nybl: We are developing an AI ecosystem that enables businesses, in any industry, to be able to deploy artificially intelligent solutions using both our platform and applications. Our mission is to develop science-based, physics-informed AI AI applications for the critical industries that humanity needs to survive and thrive. By enabling brilliant people to solve pressing humanitarian, energy, scientific, and sustainability challenges, we’re proving the power of AI to make a global impact. Our vision is to democratize AI - unlocking the full depth and breadth of human potential through our suite of AI applications, a world-class ML framework and platform that transforms data into actionable intelligence. About the role: We are seeking a highly experienced Subject Matter Expert (SME) in Electrical Submersible Pump (ESP) systems to join our team and guide the development of Lift.ai, an advanced analytical and machine learning platform for production optimization. This role is designed for a senior professional who combines deep ESP domain knowledge with the ability to collaborate on the integration of cutting-edge ML solutions that deliver unprecedented value to operators. Responsibilities: • Act as the technical authority for ESP design, operations, and optimization within n.lift • Define and validate core ESP performance calculations, physics-based workflows, and diagnostic methodologies. • Work with data scientists to embed machine learning models into engineering workflows, ensuring results are physically sound, explainable, and actionable. • Review and validate hybrid (physics + ML) algorithms for optimization, prediction, and anomaly detection. • Provide domain expertise to ensure ML outputs align with real-world ESP performance, failure modes, and operational constraints. • Collaborate with software engineers to translate ESP physics, correlations, and field practices into scalable algorithms and user-facing tools. • Mentor junior engineers and contribute to knowledge transfer across the team. • Support product strategy by identifying high-value use cases for analytics + ML integration in artificial lift management. • Represent Lift.ai as an industry thought leader in the application of advanced analytics and ML to ESP systems. Qualifications: • 10+ years of direct experience with ESP systems (design, operations, optimization, or troubleshooting). • Expertise in ESP diagnostics (power, current, voltage, vibration, performance degradation, failure mechanisms). • Solid understanding of petroleum production systems, artificial lift methods, and multiphase flow. • Experience collaborating with software/ML teams on applied AI solutions (not required to build ML models, but able to guide and validate them technically). • Strong analytical mindset with the ability to balance physics-based engineering and data-driven insights. • Excellent communication skills for cross-functional collaboration and client engagement. • Advanced degree (preferred) or equivalent experience in Petroleum, Electrical, or Mechanical Engineering. This is a strategic role at the intersection of engineering expertise and machine learning innovation. As SME, you will ensure that Lift.ai becomes the industry’s most trusted AI-driven ESP optimization and system health platform, combining the best of engineering science with the power of data.

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Responsibilities

Act as the technical authority for ESP design, operations, and optimization while collaborating with data scientists to integrate machine learning models into engineering workflows. Provide domain expertise to ensure machine learning outputs align with real-world ESP performance and operational constraints.