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For Companies At a Glance
- Tasks: Join our ML engineering team to build and optimize infrastructure for machine learning models.
- Company: Epic Games creates award-winning games and innovative engine technology for immersive experiences.
- Benefits: Enjoy 100% paid benefits, including health insurance, mental well-being programs, and more.
- Why this job: Make a positive impact on millions of players while working in a creative and collaborative environment.
- Qualifications: Experience in engineering, data analytics, and machine learning; Python proficiency is essential.
- Other info: Be part of a diverse team at a global company with over 4,500 employees.
The predicted salary is between 42000 - 84000 £ per year.
WHAT MAKES US EPIC?
At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating.
Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development.
ENGINEERING – GAMES
What We Do
Unreal projects have been leading the pack of real-time entertainment with our constantly growing team of engineering experts. We’re always improving on the tools and technology that empower content developers worldwide.
What You\’ll Do
Epic Games is looking for a Machine Learning Ops Engineer to support our team of Machine Learning engineers building solutions for internal use cases, such as classifiers for content moderation and security, semantic search, chatbots, etc. Your focus will be building reliable infrastructure for training, validating, serving, and monitoring our ML models at scale. This is an incredible opportunity to create a fun and safe environment for millions of players and make a positive impact on the Epic ecosystem.
In this role, you will
- Work directly with our ML engineering team to improve codebase architecture, performance, observability and scale.
- Operationalize proof of concept models into high availability production services, hosted on Amazon EKS, with a focus on factors such as latency, throughput and scalability.
- Build and optimize CI/CD pipelines to enable a team of 20+ engineers to ship ML models at scale, quickly and safely.
- Work with key stakeholders to identify technical debt and migrate legacy systems to the latest tools and platforms within Epic.
What we\’re looking for
- Experience with engineering, data analytics, and machine learning.
- Experience in building & maintaining technology used in ML development, with a focus on Python as the programming language.
- Experience in building and maintaining infrastructure for training and deployment of large-scale ML/DL models that scale across clusters with CPU/GPU machines.
- Experience in any of the following technologies and techniques is a plus: Pytorch, Torchserve, ONNX, Model quantization, TensorRT, OpenVINO, NVIDIA Triton.
- Fluency in Unix/Linux tooling, shell scripting and operating systems internal is a plus.
- Excellent communication and interpersonal skills.
- BS/BA degree or equivalent work experience.
EPIC JOB + EPIC BENEFITS = EPIC LIFE
We pay 100% for benefits except for PMI (for dependents). Our current benefits package includes pension, private medical insurance, health care cash plan, dental insurance, disability and life insurance, critical illness, cycle to work scheme, flu shots, health checks, and meals. We also offer a robust mental well-being program through Modern Health, which provides free therapy and coaching for employees & dependents.
ABOUT US
Epic Games spans across 25 countries with 46 studios and 4,500+ employees globally. For over 25 years, we\’ve been making award-winning games and engine technology that empowers others to make visually stunning games and 3D content that bring environments to life like never before. Epic\’s award-winning Unreal Engine technology not only provides game developers the ability to build high-fidelity, interactive experiences for PC, console, mobile, and VR, it is also a tool being embraced by content creators across a variety of industries such as media and entertainment, automotive, and architectural design. As we continue to build our Engine technology and develop remarkable games, we strive to build teams of world-class talent.
Like what you hear? Come be a part of something Epic!
Epic Games deeply values diverse teams and an inclusive work culture, and we are proud to be an Equal Opportunity employer. Learn more about our Equal Employment Opportunity (EEO) Policy here .
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Machine Learning Ops Engineer employer: Houston Texans
Contact Detail:
Houston Texans Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Ops Engineer
✨Tip Number 1
Familiarize yourself with the specific technologies mentioned in the job description, such as Pytorch and TensorRT. Having hands-on experience or projects showcasing your skills with these tools can set you apart from other candidates.
✨Tip Number 2
Engage with the Epic Games community on platforms like GitHub or relevant forums. Contributing to open-source projects or discussions related to machine learning can help you build connections and demonstrate your passion for the field.
✨Tip Number 3
Prepare to discuss your previous experiences with CI/CD pipelines and how you've optimized them for ML models. Being able to articulate your problem-solving process and the impact of your work will resonate well with the interviewers.
✨Tip Number 4
Showcase your communication skills by practicing explaining complex technical concepts in simple terms. This is crucial for collaborating with cross-functional teams at Epic Games, where clear communication is key to success.
We think you need these skills to ace Machine Learning Ops Engineer
Some tips for your application 🫡
Understand the Role: Take the time to thoroughly read the job description for the Machine Learning Ops Engineer position. Understand the key responsibilities and required skills, such as experience with Python, ML infrastructure, and CI/CD pipelines.
Tailor Your Resume: Customize your resume to highlight relevant experience in engineering, data analytics, and machine learning. Make sure to include specific projects or roles where you utilized technologies mentioned in the job description, like Pytorch or TensorRT.
Craft a Compelling Cover Letter: Write a cover letter that reflects your passion for the gaming industry and your understanding of Epic Games' mission. Mention how your skills align with their needs and express your enthusiasm for contributing to their innovative projects.
Showcase Your Communication Skills: Since excellent communication is emphasized in the job description, consider including examples in your application that demonstrate your ability to work collaboratively and effectively communicate technical concepts to diverse stakeholders.
How to prepare for a job interview at Houston Texans
✨Show Your Passion for Machine Learning
Make sure to express your enthusiasm for machine learning and how it can impact gaming and user experiences. Share any personal projects or experiences that highlight your passion and creativity in this field.
✨Demonstrate Technical Proficiency
Be prepared to discuss your experience with Python and any relevant ML technologies like Pytorch or TensorRT. Highlight specific projects where you built or maintained infrastructure for ML models, focusing on scalability and performance.
✨Communicate Effectively
Since excellent communication skills are essential, practice explaining complex technical concepts in a clear and concise manner. Be ready to discuss how you collaborate with cross-functional teams and stakeholders.
✨Prepare for Problem-Solving Scenarios
Expect to face technical challenges during the interview. Prepare to walk through your thought process on how you would approach operationalizing proof of concept models or optimizing CI/CD pipelines for ML deployment.
