Machine Learning Engineer

Job Title:

Machine Learning Engineer – Poland

About Us:

We are a global technology company, driving energy innovation for a balanced planet. At SLB, we create amazing technology that unlocks access to energy for the benefit of all. We are facing the world’s greatest balancing act—how to simultaneously reduce emissions and meet the world’s growing energy demands. Our collective future depends on decarbonizing the fossil fuel industry while innovating a new energy landscape. It’s what drives us. Ensuring progress for people and the planet, on the journey to net zero and beyond.

More than 98,000 employees across 120 countries have already started their SLB journeys. Start yours now!

If you would like to know more, please visit our website at www.slb.com.

Location:

Warsaw, Poland

Duration:

Permanent

Work Schedule:

Hybrid

Job Summary:

We are seeking a Machine Learning Engineer to join SLB’s digital and AI community in Poland.

In this role, you will be responsible for guiding the successful development, automation and governance of AI and Machine Learning solutions for internal customers. You will work closely with Data Scientists, Domain Experts and Product Owners to build and deploy end-to-end AI products across the organization.

This position requires strong working knowledge of cloud platforms, DevOps / MLOps practices, machine learning and deep learning technologies, and application development. You will also contribute to the formulation of business requirements and help establish best practices for building scalable and reliable AI solutions.

Responsibilities and Duties:

• Develop and maintain production-ready machine learning and software code.
• Leverage GPU and CPU resources effectively and assess capacity requirements for ML workloads.
• Architect, automate and orchestrate DevOps and MLOps pipelines on cloud platforms.
• Design and build reusable tools and frameworks to monitor, optimize and maintain ML solutions.
• Design and implement data engineering and ETL pipelines.
• Collaborate with cross-functional teams to understand business problems and ensure scalability, business continuity and appropriate delivery timelines.
• Support the end-to-end lifecycle of AI products, from development to deployment and monitoring.
• Conduct internal workshops and participate in external meetups and conferences, including giving technical talks.
• Continuously stay up to date with the latest AI, machine learning and related technologies and proactively upskill as needed.

Essential Requirements:

• Bachelor’s or Master’s degree in Computer Science, Operations Research, Mathematics or a related field, with 3–4 years of relevant professional experience.
• Strong experience working with large and diverse datasets.
• Excellent programming skills, with strong proficiency in Python.
• Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Experience in cloud application development (Google Cloud Platform and/or Microsoft Azure preferred).
• Strong problem-solving, debugging and troubleshooting skills.
• Experience designing, building and maintaining ETL workflows and data pipelines using orchestration tools such as Apache Airflow, Kubeflow or similar.
• Solid understanding of DevOps practices, including CI/CD and Git-based workflows.
• Comfortable working in Unix/Linux environments.
• Hands-on experience designing, building and supporting RESTful APIs.
• Working knowledge of machine learning and deep learning concepts, including experience supporting model deployment and monitoring.

Preferred Skills:

• Experience with deep learning frameworks such as Keras, TensorFlow and/or PyTorch.
• Strong understanding of key machine learning and deep learning algorithms.
• Knowledge of oilfield terminology and business practices.
• Experience with IoT and edge technologies.
• Recognized open-source contributions.
• Familiarity with modern data engineering tools such as Flink, Spark or Kafka.
• Experience with big data technologies (Spark, Hadoop, Storm, Hive, Pig) and NoSQL data stores.