AI Engineer, Industrial

About the role

Develop practical AI, machine learning and optimization solutions that help technical and operational teams make complex industrial data easier to understand, explore, and act on.

Apply frontier AI models, including large language models, foundation models, and multimodal approaches, to create reliable tools for search, analysis, knowledge discovery, and decision support.

Build AI-enabled applications that combine models, data pipelines, prompts, agents, evaluations, optimization logic, and user feedback into solutions that are useful, explainable, and maintainable.

Design and use knowledge graphs, ontologies, and structured domain knowledge to connect process data, experimental data, documents, terminology, assets, materials, and business context.

Collaborate across data science, engineering, digital technology, operations, and business teams to deliver solutions that create measurable value and are designed for adoption.

Responsibilities

• Develop practical AI, machine learning and optimization solutions that help technical and operational teams make complex industrial data easier to understand, explore, and act on.
• Apply frontier AI models, including large language models, foundation models, and multimodal approaches, to create reliable tools for search, analysis, knowledge discovery, and decision support.
• Build AI-enabled applications that combine models, data pipelines, prompts, agents, evaluations, optimization logic, and user feedback into solutions that are useful, explainable, and maintainable.
• Design and use knowledge graphs, ontologies, and structured domain knowledge to connect process data, experimental data, documents, terminology, assets, materials, and business context.
• Collaborate across data science, engineering, digital technology, operations, and business teams to deliver solutions that create measurable value and are designed for adoption.

Requirements

• Master’s, or PhD degree in Chemical Engineering, Biochemical Engineering, Bioinformatics, Process Control, Computer Science, Data Science or Applied Mathematics.
• Demonstrated industrial experience with a proven track record of delivering measurable business impact and driving outcomes in production or operational environments.
• Proven, hands-on experience with Python and modern machine learning workflows, including data preparation, modeling, validation, deployment, and monitoring.
• Experience developing end-to-end AI or machine learning applications, not only exploratory notebooks, with an ability to move from prototype to reliable solution including demonstrated experience deploying solutions in real industrial or production environments.
• Practical understanding of frontier model capabilities and limitations, including prompt design, agentic AI, retrieval-augmented generation, evaluation, hallucination reduction, and human-in-the-loop workflows.
• Experience working with structured and unstructured data, such as time-series data, scientific data, engineering data, documents, knowledge bases, or complex operational datasets.
• Knowledge of methods such as mathematical optimization, process control, forecasting, anomaly detection, recommendation systems, natural language interfaces, classification, deep learning, or predictive analytics.
• Familiarity with software engineering practices such as version control, testing, application programming interfaces, containers, continuous integration and continuous delivery, and maintainable code design.
• Ability to communicate clearly with both technical and non-technical audiences, including explaining model outputs, uncertainty, assumptions, control logic, optimization trade-offs, and practical implications.
• A practical, delivery-oriented mindset with curiosity for industrial, scientific, optimization, and process control problems and a strong focus on usefulness, trust, and adoption.

Qualifications

• Experience with knowledge graphs, ontologies, semantic modeling, graph databases, or retrieval-augmented generation for scientific, industrial, or operational use cases.
• Experience in manufacturing, process development, industrial operations, supply chain, biomanufacturing, bioinformatics, chemical processes, advanced process control, or mathematical optimization in technical environments.
• Experience with model registries, experiment tracking, observability, prompt and version management, evaluation frameworks, cloud platforms, or production machine learning systems.

Skills

• Practical understanding of frontier model capabilities and limitations, including prompt design, agentic AI, retrieval-augmented generation, evaluation, hallucination reduction, and human-in-the-loop workflows.
• Experience working with structured and unstructured data, such as time-series data, scientific data, engineering data, documents, knowledge bases, or complex operational datasets.
• Knowledge of methods such as mathematical optimization, process control, forecasting, anomaly detection, recommendation systems, natural language interfaces, classification, deep learning, or predictive analytics.
• Familiarity with software engineering practices such as version control, testing, application programming interfaces, containers, continuous integration and continuous delivery, and maintainable code design.
• Ability to communicate clearly with both technical and non-technical audiences, including explaining model outputs, uncertainty, assumptions, control logic, optimization trade-offs, and practical implications.

Benefits

• Medical
• Dental
• Vision
• 401(k)
• Vacation
• Holidays
• Paid parental leave (maternity and paternity)
• Annual bonus plan

Pay

$100,000 - $120,000 based on a variety of factors including but not limited to work experience, skills, certifications, and location.

Schedule

Remote-based working model with meaningful collaboration across global teams and regular travel for high-impact engagement.

Why Choose Us

• Opportunity to apply advanced AI, optimization, and machine learning to real industrial and scientific challenges.
• A role where software engineering, data science, domain knowledge, process control, and practical problem solving come together.
• Exposure to diverse technical communities across Health & Biosciences, operations, digital technology, engineering, and business teams.
• Learning and development opportunities in a global company with a strong innovation culture.
• A collaborative environment where useful, explainable, and trusted solutions matter.