AI/ML Engineer

Job Description

Data AI/ML (Artificial Intelligence and Machine Learning) Engineering involves the use of algorithms and statistical models to enable systems to analyze data, learn patterns, and make data-driven predictions or decisions without explicit human programming. AI/ML applications leverage vast amounts of data to identify insights, automate processes, and solve complex problems across a wide range of fields, including healthcare, finance, e-commerce, and more. AI/ML processes transform raw data into actionable intelligence, enabling automation, predictive analytics, and intelligent solutions. Data AI/ML combines advanced statistical modeling, computational power, and data engineering to build intelligent systems that can learn, adapt, and automate decisions.

We are an ambitious data product team and are looking for a AI/ML Engineer to join us in our journey!

Let us start by introducing ourselves and then move on to you and your potential new role.

The team - who are we

We are an ambitious data product team of diverse problem-solvers with a drive for getting the job done, and a shared passion to use data, data science (DS), machine learning (ML) and engineering excellence to make a difference for our customers.

Concretely, we develop data products for the Maersk.com platform for our internal and external customers.

We are a team, not a collection of individuals. We value our diverse backgrounds, our different personalities and strengths & weaknesses. We value trust and passionate debates. We challenge each other and hold each other accountable. We uphold a caring feedback culture to help each other grow, professionally and personally.

Our new member - who are you

You are passionate about cracking hard business problems with data science and ML excellence. You combine business acumen and a drive for impact with hands‑on model development and experimentation to create data products that matter.

You are a go‑getter, a challenger, learner, collaborator and shaper. Your curiosity might lead you right after reading this posting to Maersk.com to explore one of our (and your potential future) channels of impact from the user perspective.

You are, like us, a team player who cares about your team members, about growing professionally and personally, about helping your team mates grow, and about having fun together.

If this so far speaks to you, you should absolutely read on for more details.

About the role

As AI/ML Engineer, you will be part of our cross‑functional data product team, developing ML‑powered applications for our Maersk.com platform based on strong scientific rigor and engineering principles. You will collaborate closely with our colleagues from the Maersk.com platform.

The data we work with is collected and validated from various streams and used to develop sophisticated ML models (e.g., segmentation, recommender systems, ranking, propensity).

You’ll contribute across the full model lifecycle: problem framing, feature engineering, modeling, evaluation, deployment, online experimentation (A/B tests), and production monitoring.

Your responsibilities include

• Frame problems and define outcomes: Translate customer/business objectives into clear DS problems with measurable success metrics.

• Build production‑grade models: Design, train, and validate models for both batch scoring and real‑time inference.

• Feature engineering at scale: Partner with data engineers to design robust feature pipelines, feature stores, and data contracts for reliable online/offline parity.

• Experimentation & causal measurement: Design and run A/B tests, multi‑armed bandits, and holdout analyses; ensure rigorous statistical evaluation, guardrails, and risk controls.

• MLOps & model lifecycle: Use model registries, CI/CD, automated evaluations, drift/decay monitoring, and canary rollouts to deploy safely and iterate quickly.

• Documentation & reproducibility: Keep experiments, datasets, and models well‑documented and reproducible; champion high‑quality scientific and engineering practices.

• Cross‑functional collaboration: Work closely with product, platform engineering, and analytics to align on customer outcomes, explain model behavior, and communicate trade‑offs to non‑technical audiences.

• Responsible AI: Consider fairness, bias, explainability, privacy, and compliance when designing and deploying models.

• Continuous improvement: Independently direct your time and resources with engineers and fellow data scientists in the team; contribute to transforming how we work as part of our broader cultural transformation.

Skills & experience we are looking for

• Education: Bachelor’s degree, ideally in computer science, statistics, mathematics, engineering, or a related field. The ideal person may possess an advanced degree focusing on ML, applied statistics, or distributed systems.

• Experience: 4–6 years industry experience in data science or ML engineering within a software or analytics‑intensive B2B environment, delivering models into production as part of a product team.

• ML expertise: Solid understanding and experience with batch and real‑time ML solutions, especially personalization, recommendations, ranking, and propensity modeling at scale.

• Languages & tooling: Strong hands‑on skills in Python and SQL. Experience with PySpark/Spark, and ML libraries such as scikit‑learn, TensorFlow, PyTorch, and XGBoost/LightGBM.

• Data & platforms: Working knowledge of Databricks, Spark Structured Streaming, feature stores, experiment platforms, and MLflow/model registries.

• Cloud & infrastructure: Familiarity with Azure, GCP, or AWS; containerization and orchestration (Docker, Kubernetes) and pub/sub systems (e.g., Kafka) for real‑time features/inference.

• Experimentation & statistics: Experience designing and analyzing A/B tests, understanding of confidence intervals, power, uplift, and common pitfalls (e.g., peeking, interference).

• Data product mindset: Ability to compellingly present technical topics to non‑technical audiences; strong problem‑solver and self‑starter comfortable navigating ambiguity.

Good to have / we value familiarity with:

• CI/CD, DevOps, microservices, monitoring, git and source control, effective documentation; insight sharing; data modeling (dimensional/BI), ETL, data quality; responsible AI and model observability.

About the company

A.P. Moller - Maersk is the globally leading integrated container logistics company and responsible for moving 20% of global trade every year. Our over 80,000 employees work across 130 countries to connect and simplify global trade, and help our customers grow and thrive. We are right now driving an industry-defining data and technology transformation that is enabling us to deliver value to our customers with best-in-class technology, data & data products. This is a unique opportunity to further grow your career within data and technology in an iconic company with Danish roots and global reach.

What we offer

This position offers a great opportunity to apply and further develop your cutting-edge knowledge of data science and machine learning to create results and insights that are transforming the transport and logistics industry. Believe us - there has never been a more exciting time to be part of the data & technology area in the shipping & logistics industry!

Our team is embracing a flexible hybrid work setup, where we like to work from home, and equally much like to meet weekly together in the office.

As a AI/ML Engineer with Maersk, you will be part of the community of data & engineering practitioners across the company, where we develop the foundations of our future business.

We operate in a fast-paced environment utilizing modern technologies.

We embrace innovation methods where we have a close dialogue with end users, make early use of mock-ups & POCs and are committed to incremental development.

We value customer outcomes and are passionate about using technology to solve problems.

We are a diverse team with colleagues from different backgrounds and cultures.

We offer the freedom, and responsibility, to shape the setup and the processes we use in our community.

We support continuous learning, including through conferences, workshops and meetups.

Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.

We are happy to support your need for any adjustments during the application and hiring process. If you need special assistance or an accommodation to use our website, apply for a position, or to perform a job, please contact us by emailing accommodationrequests@maersk.com.

CORE SKILLS Data Analysis: The process of inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making Proficiency Level: Proficient Statistical Analysis: The process of collecting and analyzing data to identify patterns and trends, and to make informed decisions. Proficiency Level: Proficient AI & Machine Learning: The field of artificial intelligence (AI) involves creating systems that can perform tasks that typically require human intelligence. Machine learning (ML) is a subset of AI that uses algorithms to learn from and make predictions based on data Proficiency Level: Proficient Programming: Writing code to manipulate, analyze, and visualize data, often using languages like Python, R, and SQL. Proficiency Level: Proficient Data Science: A multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Proficiency Level: Proficient SPECIALIZED SKILLS Data Validation and Testing: Ensuring that data is accurate and meets the required standards before it is used in analysis or decision-making. Model Deployment: The process of making a trained machine learning model available for use in production environments. Machine Learning Pipelines: Automated workflows that manage the end-to-end process of training and deploying machine learning models. Deep Learning: A subset of machine learning involving neural networks with many layers, used to model complex patterns in data. Natural Language Processing (NLP): A field of AI that focuses on the interaction between computers and humans through natural language. Optimization & Scientific Computing: Using Mathematical techniques and computational algorithms to solve complex problems and optimize processes Decision Modeling and Risk Analysis: Decision Modeling and Risk Analysis are methodologies used to make informed, data-driven decisions under uncertainty, especially when multiple factors and possible outcomes need to be considered. Technical Documentation: Creating and maintaining documentation that explains the functionality, use, and maintenance of software or systems. Definition of Proficiency Levels: Foundational: This is the entry level of the skill, typically expected when starting a new role or working with the skill for the first time. You rely on strong manager support, coaching, and training as you build the capability to progress to higher proficiency levels. Proficient: This is the level at which you are considered effective in the skill. You demonstrate more than just functional competence—you begin to have a noticeable impact in your role by applying the skill consistently and meaningfully. You require only minimal support, coaching, or training to apply the skill successfully. Advanced: This is the level where you move beyond meeting expectations to actively leading, influencing, and delivering considerable impact across the wider business. You are seen as a role model, demonstrate the skill independently, and require little to no manager support.