The company is looking to automate and optimize the drilling process by developing intelligent systems that can make real-time, data-driven decisions to enhance safety, increase efficiency, and maximize economic value in oil and gas operations.
Requirements
- Strong theoretical and practical knowledge of core reinforcement learning concepts and algorithms (e.g., Deep Q-Networks, Proximal Policy Optimization, Actor-Critic methods).
- Proficiency in Python and experience with at least one major deep learning framework (PyTorch or TensorFlow).
- A solid understanding of machine learning fundamentals, statistics, and optimization.
- Hands-on experience or a strong understanding of drilling operations or geomechanics.
- Proven experience applying RL to real-world control systems, robotics, or other physical systems with dynamic environments.
- Experience with drilling or reservoir simulation software (e.g., COMPASS, DrillSim, Nexus) or other physics-based simulators.
- A track record of publications in relevant conferences or industry journals (e.g., SPE Journal).
Responsibilities
- Design, development, and deployment of intelligent systems that automate and optimize the drilling process.
- Build and train agents to make real-time, data-driven decisions that enhance safety, increase efficiency, and maximize the economic value of our customers’ drilling operations.
- Apply state-of-the-art AI techniques to a high-impact oil and gas domain, transforming how we discover and produce energy.
Other
- Master's or Ph.D. in Computer Science, Petroleum Engineering, Mechanical Engineering, or a related quantitative field.
- Experience Level: Experienced Hire
- Job Family: Engineering/Science/Technology
- Product Service Line: Landmark Software & Services
- Full Time / Part Time: Full Time
