Senior Machine Learning Engineer

About this role

<p>At 2K, we create some of the most iconic and culture-shaping video games in entertainment, including <strong><em>NBA® 2K</em></strong>, one of the top-selling franchises in the world, and legendary titles like <strong><em>BioShock®</em></strong><em>, </em><strong><em>Borderlands®</em></strong><em>, </em><strong><em>Mafia</em></strong><em>, </em><strong><em>Sid Meier’s Civilization</em></strong><strong>®</strong>, and<em> </em><strong><em>XCOM®</em></strong>, as well as fan favorites <strong><em>WWE® 2K</em></strong><em>, </em><strong><em>TopSpin®</em></strong>, and <strong><em>PGA TOUR® 2K</em></strong><em>.</em> We build unforgettable experiences by pushing the boundaries of creativity, authenticity and innovation across every genre.</p> <p>Our portfolio is brought to life by some of the most influential game development studios in the world. <strong>Visual Concepts, Firaxis Games, Hangar 13, Cat Daddy Games, 31st Union, Cloud Chamber, Gearbox, HB Studios, </strong>and <strong>2K SportsLab</strong> create world-class experiences across platforms.</p> <p>But what truly powers 2K is our people.</p> <p>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 <strong>come as they are</strong> and do their best work.&nbsp;</p> <p><strong>What We Need</strong></p> <p>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.</p> <p>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.<strong><em><span lang="~EN-US~"><br></span></em></strong></p> <p><strong>What You Will Do</strong></p> <ul> <li>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.&nbsp;</li> <li>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.&nbsp;</li> <li>Design and rapidly prototype ML-powered products for applications including recommenders, matchmaking, cheat/toxicity intervention, and economy balancing.&nbsp;</li> <li>Work closely with studio devs and central tech to plan and execute the integration of ML applications into games on launch timelines.&nbsp;</li> <li>Identify, evaluate, and pilot opportunities to apply&nbsp; GenAI/LLM to enable new use cases, and translate promising concepts into secure, scalable, and measurable solutions.</li> </ul> <p><strong>What Will Make You A Great Fit</strong></p> <ul> <li>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.</li> <li>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.</li> <li>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.&nbsp;</li> <li>Hands-on with modern ML frameworks such asPyTorch, TensorFlow, scikit-learn, or Spark ML.&nbsp;</li> <li>Production experience with cloud infrastructure (AWS, GCP, or Azure), containers and orchestration (e.g. Kubernetes), serverless, and microservice architecture.&nbsp;</li> <li>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.&nbsp;</li> <li>Experience with modern data technologies such as relational and NoSQL databases, and lakehouse/big-data tooling such as Apache Spark or Databricks/Delta.&nbsp;</li> <li>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.</li> </ul> <p><strong>Nice To Have</strong></p> <ul> <li>Experience with recommender systems, search algorithms, matchmaking, or reinforcement learning technologies.&nbsp;</li> <li>Experience with infrastructure-as-code and infrastructure automation tools,&nbsp; such as Terraform, or AWS CloudFormation.&nbsp;</li> <li>Experience with managed ML platforms such as Amazon SageMaker or Databricks.</li> <li>Familiarity with stream processing tools such as Apache Kafka, Kinesis, or Spark Streaming.&nbsp;</li> <li>Familiarity with&nbsp; GenAI and LLM application patterns and technologies,&nbsp; including RAG, evaluation and guardrails, agentic workflows, or vector databases.&nbsp;</li> <li>Major game engines such as Unreal or Unity.</li> </ul> <p>As an equal opportunity employer, we are committed to ensuring that qualified individuals with disabilities are provided reasonable accommodati