Senior Machine Learning Engineer

At 2K, we create some of the most iconic and culture-shaping video games in entertainment, including NBA® 2K, one of the top-selling franchises in the world, and legendary titles like BioShock®, Borderlands®, Mafia, Sid Meier’s Civilization****®, and XCOM®, as well as fan favorites WWE® 2K, TopSpin®, and PGA TOUR® 2K. We build unforgettable experiences by pushing the boundaries of creativity, authenticity and innovation across every genre.

Our portfolio is brought to life by some of the most influential game development studios in the world. Visual Concepts, Firaxis Games, Hangar 13, Cat Daddy Games, 31st Union, Cloud Chamber, Gearbox, HB Studios, and 2K SportsLab create world-class experiences across platforms.

But what truly powers 2K is our people.

We believe the best ideas come from teams that feel empowered, supported, and inspired. As an equal opportunity employer, we are committed to fostering a diverse, inclusive workplace where people are encouraged to come as they are and do their best work.

What We Need

You are an exceptional software engineer with a strong track record of deploying and operating ML models in production, particularly as low-latency, high-availability prediction services. You can deploy ML models as real-time, near-real-time, or batch services depending on the requirements of the use case. You bring deep, hands-on machine learning experience with a working command of ML algorithm types and tasks, the end-to-end ML lifecycle, and modern modeling and serving frameworks.

You operate with autonomy and judgment. You're a solution-oriented, creative problem solver and a self-starter who drives initiatives end-to-end: scoping ambiguous problems, making sound architectural choices across multi-component systems, and shipping to deadline. As a senior engineer, you raise the bar around you by setting technical direction, mentoring others, and championing engineering standards across the team and wider ML community.

What You Will Do

• Partner with ML scientists, data engineers, central tech, and game studios to deploy ML models as production-grade decision services and integrate them into larger systems and live products.
• Own and mature Machine Learning Ops practice: CI/CD for models, model registry and feature stores, real-time inference at scale, monitoring, drift detection, and reproducibility. Advocate for engineering best practices across the community.
• Design and rapidly prototype ML-powered products for applications including recommenders, matchmaking, cheat/toxicity intervention, and economy balancing.
• Work closely with studio devs and central tech to plan and execute the integration of ML applications into games on launch timelines.
• Identify, evaluate, and pilot opportunities to apply GenAI/LLM to enable new use cases, and translate promising concepts into secure, scalable, and measurable solutions.

What Will Make You A Great Fit

• Bachelor's degree in Computer Science, Computer/Electrical Engineering, or a related STEM field and 4+ years in software development/engineering, including substantial experience deploying ML in production, or Master's degree with 2-4 years of relevant experience.
• Strong programming skills, proficient in Python. Familiarity with a high-performance systems language (C, C++, Java, Rust, …) is a plus. Comfortable across object-oriented and functional paradigms, and quick to pick up new languages as needed.
• Solid grounding in common ML tasks (supervised and unsupervised; reinforcement learning a plus) and commonly used algorithms across traditional ML and deep learning, with the ability to learn new approaches quickly.
• Hands-on with modern ML frameworks such asPyTorch, TensorFlow, scikit-learn, or Spark ML.
• Production experience with cloud infrastructure (AWS, GCP, or Azure), containers and orchestration (e.g. Kubernetes), serverless, and microservice architecture.
• Hands-on with MLOps and CI/CD tooling including model registries, feature stores, pipeline orchestration (e.g. Airflow, Kubeflow, or MLflow), and automated training, deployment, and monitoring.
• Experience with modern data technologies such as relational and NoSQL databases, and lakehouse/big-data tooling such as Apache Spark or Databricks/Delta.
• Flexibility to start and end late to offer a couple of hours overlap with our US HQ (core hours approximately 10:00–18:30 local) to enable close collaboration.

Nice To Have

• Experience with recommender systems, search algorithms, matchmaking, or reinforcement learning technologies.
• Experience with infrastructure-as-code and infrastructure automation tools, such as Terraform, or AWS CloudFormation.
• Experience with managed ML platforms such as Amazon SageMaker or Databricks.
• Familiarity with stream processing tools such as Apache Kafka, Kinesis, or Spark Streaming.
• Familiarity with GenAI and LLM application patterns and technologies, including RAG, evaluation and guardrails, agentic workflows, or vector databases.
• Major game engines such as Unreal or Unity.

As an equal opportunity employer, we are committed to ensuring that qualified individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform their essential job functions, and to receive other benefits and privileges of employment. Please contact us if you need reasonable accommodation.

Please note that 2K Games and its studios never uses instant messaging apps or personal email accounts to contact prospective employees or conduct interviews and when emailing, only use 2K.com accounts.

#LI-Hybrid

Bachelor's degree in Computer Science or related STEM field and 4+ years in software development/engineering, including substantial experience deploying ML in production, or Master's degree with 2-4 years of relevant experience. Strong programming skills, proficient in Python. Familiarity with a high-performance systems language (C, C++, Java, Rust, …) is a plus. Solid grounding in common ML tasks (supervised and unsupervised; reinforcement learning a plus) and commonly used algorithms across traditional ML and deep learning. Hands-on with modern ML frameworks such as PyTorch, TensorFlow, scikit-learn, or Spark ML. Production experience with cloud infrastructure (AWS, GCP, or Azure), containers and orchestration (e.g. Kubernetes), serverless, and microservice architecture. Hands-on with MLOps and CI/CD tooling including model registries, feature stores, pipeline orchestration (e.g. Airflow, Kubeflow, or MLflow), and automated training, deployment, and monitoring. Experience with modern data technologies such as relational and NoSQL databases, and lakehouse/big-data tooling such as Apache Spark or Databricks/Delta. Flexibility to start and end late to offer a couple of hours overlap with our US HQ.