Machine Learning & AI Engineer – Drilling Services

Overview

Our engineering teams are at the forefront of the technical challenges facing today’s Energy Industry. As a Machine Learning & AI Engineer you design, develop, and deploy intelligent models and AI-driven systems for real‑world applications. This role focuses on applied machine learning, deep learning, and intelligent agent systems, with an emphasis on data quality, interpretability, and scalable deployment.

The ideal candidate has hands‑on experience building production‑ready ML models, working with time‑series, multimodal, and sensor data, and collaborating across research, engineering, and product teams to translate advanced algorithms into reliable solutions.

Key Responsibilities Design, develop, train, and validate machine learning and deep learning models for classification, prediction, anomaly detection, and decision‑making tasks Build end‑to‑end ML pipelines, including data preprocessing, feature engineering, model training, evaluation, and optimization Develop and deploy AI agent systems, including multi‑agent orchestration, tool calling, and workflow automation Apply time‑series modeling and sensor data analytics to solve predictive maintenance, health assessment, or operational intelligence problems Implement Retrieval‑Augmented Generation (RAG) and LLM‑based systems with a focus on factual accuracy and hallucination reduction Ensure model interpretability, explainability, and performance monitoring for production use Collaborate with cross‑functional teams (research, engineering, product) to integrate models into real‑world systems Document methodologies, experiments, and results clearly for technical and non‑technical stakeholders Knowledge, Skills & Abilities

CORE MACHINE LEARNING & AI

Strong experience in Machine Learning and Deep Learning, including classical ML and neural network‑based approaches Hands‑on experience with time‑series data, multimodal data, and sensor‑based datasets Experience with transformer architectures, mixture‑of‑experts (MoE), and attention mechanisms Knowledge of anomaly detection, predictive modeling, and remaining useful life (RUL) estimation Experience with reinforcement learning and multi‑agent systems

AI SYSTEMS & LLMS

Experience building AI agents and intelligent workflows Practical experience with Large Language Models (LLMs), prompt engineering, and tool/function calling Experience implementing RAG pipelines using vector search and structured knowledge retrieval

ENGINEERING & TOOLING

Strong programming skills in Python; familiarity with C/C++ and MATLAB is a plus Experience with ML frameworks and libraries such as PyTorch, scikit‑learn, NumPy, pandas Experience with FastAPI or API‑based ML services Familiarity with workflow automation tools and ML experimentation frameworks Experience & Education

Required

Ph.D. or Master’s degree in Machine Learning, Computer Science, Engineering; Ph.D. preferred. Related technical field (or equivalent applied research and industry experience). Travel Requirement

This role may require domestic and potentially international travel of up to: 10-25%

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