You Will:

• Implement groundbreaking features in Roblox game engine to build the best avatar system in the world
• Build APIs for Roblox developers enabling in-experience creation of Roblox Avatars from scratch
• Enhance avatar fidelity and uniqueness on Roblox
• Use the Roblox game engine to build excellent vertical applications for multiple platforms (mobile, tablet, PC, and console)
• Refactor and improve our existing codebase, and establish architectural standards for system reliability, performance, and test coverage
• Work with numerous teams to help build the world's best user generated gaming platform
• Implement systems in a combination of C++ and Lua

You Have:

• 8+ years of professional experience working on a consumer-facing game engine and/or game application
• Proficiency with C++, 3D math, data structures, algorithms, and networking
• Experience working on character building and customization systems, editors, and content creation tools
• Experience building online console or mobile game titles
• Experience with game or film character systems such as animation, rigid-body or cloth/hair physics, or shading

WHAT MAKES US EPIC?

QUALITY ASSURANCE

What We Do

What You'll Do

In this role, you will

• Lead and grow a team of SDETs building automated test coverage across Unreal Engine, as well as mentor and coach SDETS at every career stage
• Write and ship production code in the team's test frameworks, tools, and CI integrations, as well as own the team's systems end to end, including design, reliability, and maintenance
• Set the team's test roadmap, from unit and API coverage through input-driven end-to-end tests of the editor and runtime
• Extend existing frameworks, including low-level, integration, and end to end tests
• Turn manual test cases into automated coverage, as well as keep committed tests deterministic and reproducible on any machine
• Leverage Al-assisted tools to speed up tool/framework development, test authoring, failure triage, and flake analysis
• Increase visibility into test coverage and Cl health for engineering leadership, and influence the vision for the wider SDET organization at Epic

What we're looking for

• Experience leading software engineering teams, including mentorship, technical roadmaps, trade-offs, and prioritization
• A track record of writing and shipping production code while leading a team, as well as current, hands-on proficiency in C++ and at least one of C#, Python, or TypeScript
• Strong knowledge of how game engines work. Unreal Engine 5 experience is preferred
• Experience with automation frameworks and continuous integration at scale, and experience setting test strategy across unit, integration, and end-to-end levels
• Experience using AI coding tools in engineering work, and judgment about when their output can be trusted
• Ability to learn complex tech stacks quickly and extend existing investments rather than bypass them
• Clear written communication with engineers, producers, and leadership

EPIC JOB + EPIC BENEFITS = EPIC LIFE

ABOUT US

Like what you hear? Come be a part of something Epic!

About the Role

What You'll Do

• Serve as the deep technical authority on Generative AI, LLMs, and applied machine learning for the EMEA Emerging DNB business unit, supporting the most strategic and complex customer engagements across our digital-native and born-in-the-cloud accounts.
• Partner with our EMEA Emerging DNB Solutions Architects, Solutions Engineers, and Account teams to scope, design, and de-risk GenAI use cases — from retrieval-augmented generation and agentic systems to fine-tuning and evaluation.
• Build hands-on proofs of concept and reference implementations that operationalise large-scale LLM and deep learning workloads on Databricks (Mosaic AI, MLflow, Model Serving, Vector Search, Unity Catalog governance), tuned to the fast iteration cycles Emerging DNB customers expect.
• Lead fine-tuning and model-customisation engagements on open LLMs (e.g., Llama-family models), including judge-based and label-efficient evaluation approaches for domains where quality and safety are paramount.
• Act as a bridge to Product and Engineering — channel field and customer feedback into the roadmap and represent the roadmap back to the field.
• Enable and mentor the broader EMEA Emerging DNB Field Engineering team through workshops, reference architectures, and internal enablement, multiplying GenAI expertise across our ~70-person SA/SE organisation.
• Represent Databricks externally as a technical thought leader — conference talks (e.g., Data + AI Summit), blogs, and customer executive briefings.

What We're Looking For

• Strong understanding of the LLM landscape, including leading proprietary and open-source models and providers, with the ability to differentiate their capabilities, trade-offs, and suitability for different use cases, and to articulate a clear, informed point of view (POV) to customers.
• Deep expertise in the modern GenAI stack: LLM application patterns (RAG, agents, tool use), fine-tuning and model customization, prompt and evaluation engineering, and LLM guardrails/safety.
• Strong foundations in machine learning and deep learning, including distributed training, GPU workloads, and the full MLOps lifecycle (tracking, registry, serving, monitoring).
• Proficiency in Python and the ML ecosystem (PyTorch/Transformers, MLflow, Spark), and comfort building production-quality reference implementations.
• Experience with the Databricks platform — or the ability to ramp on it quickly — including Mosaic AI, Model Serving, Vector Search, and Unity Catalog. Cloud experience across AWS, Azure, or GCP.
• Excellent communication and consultative skills: able to earn the trust of both hands-on engineers and senior technical executives, and to explain complex GenAI trade-offs clearly.
• A track record of technical leadership and mentorship — someone who elevates the people around them.
• Comfort operating at the pace of digital-native and high-growth customers, where speed, iteration, and technical credibility win the deal.
• Based in the United Kingdom and able to work across EMEA time zones, with willingness to travel to customer sites within the region as needed.
• Background helping digital-native customers build data and ML solutions (e.g., prior experience at a data/ML platform or consulting vendor) is a strong plus.

Nice to Have

• Public technical presence: conference speaking, published talks, blogs, or open-source contributions in the ML/GenAI space.
• Domain depth in a regulated or high-stakes industry (healthcare/life sciences, financial services) where model quality, evaluation, and safety are critical.
• Experience partnering with Product and Engineering teams to influence roadmap.

Why This Role

About the Role

What You'll Do

• Serve as the deep technical authority on Generative AI, LLMs, and applied machine learning for the EMEA Emerging DNB business unit, supporting the most strategic and complex customer engagements across our digital-native and born-in-the-cloud accounts.
• Partner with our EMEA Emerging DNB Solutions Architects, Solutions Engineers, and Account teams to scope, design, and de-risk GenAI use cases — from retrieval-augmented generation and agentic systems to fine-tuning and evaluation.
• Build hands-on proofs of concept and reference implementations that operationalise large-scale LLM and deep learning workloads on Databricks (Mosaic AI, MLflow, Model Serving, Vector Search, Unity Catalog governance), tuned to the fast iteration cycles Emerging DNB customers expect.
• Lead fine-tuning and model-customisation engagements on open LLMs (e.g., Llama-family models), including judge-based and label-efficient evaluation approaches for domains where quality and safety are paramount.
• Act as a bridge to Product and Engineering — channel field and customer feedback into the roadmap and represent the roadmap back to the field.
• Enable and mentor the broader EMEA Emerging DNB Field Engineering team through workshops, reference architectures, and internal enablement, multiplying GenAI expertise across our ~70-person SA/SE organisation.
• Represent Databricks externally as a technical thought leader — conference talks (e.g., Data + AI Summit), blogs, and customer executive briefings.

What We're Looking For

• Strong understanding of the LLM landscape, including leading proprietary and open-source models and providers, with the ability to differentiate their capabilities, trade-offs, and suitability for different use cases, and to articulate a clear, informed point of view (POV) to customers.
• Deep expertise in the modern GenAI stack: LLM application patterns (RAG, agents, tool use), fine-tuning and model customization, prompt and evaluation engineering, and LLM guardrails/safety.
• Strong foundations in machine learning and deep learning, including distributed training, GPU workloads, and the full MLOps lifecycle (tracking, registry, serving, monitoring).
• Proficiency in Python and the ML ecosystem (PyTorch/Transformers, MLflow, Spark), and comfort building production-quality reference implementations.
• Experience with the Databricks platform — or the ability to ramp on it quickly — including Mosaic AI, Model Serving, Vector Search, and Unity Catalog. Cloud experience across AWS, Azure, or GCP.
• Excellent communication and consultative skills: able to earn the trust of both hands-on engineers and senior technical executives, and to explain complex GenAI trade-offs clearly.
• A track record of technical leadership and mentorship — someone who elevates the people around them.
• Comfort operating at the pace of digital-native and high-growth customers, where speed, iteration, and technical credibility win the deal.
• Based in the United Kingdom and able to work across EMEA time zones, with willingness to travel to customer sites within the region as needed.
• Background helping digital-native customers build data and ML solutions (e.g., prior experience at a data/ML platform or consulting vendor) is a strong plus.

Nice to Have

• Public technical presence: conference speaking, published talks, blogs, or open-source contributions in the ML/GenAI space.
• Domain depth in a regulated or high-stakes industry (healthcare/life sciences, financial services) where model quality, evaluation, and safety are critical.
• Experience partnering with Product and Engineering teams to influence roadmap.

Why This Role

About the Role

What You'll Do

• Serve as the deep technical authority on Generative AI, LLMs, and applied machine learning for the EMEA Emerging DNB business unit, supporting the most strategic and complex customer engagements across our digital-native and born-in-the-cloud accounts.
• Partner with our EMEA Emerging DNB Solutions Architects, Solutions Engineers, and Account teams to scope, design, and de-risk GenAI use cases — from retrieval-augmented generation and agentic systems to fine-tuning and evaluation.
• Build hands-on proofs of concept and reference implementations that operationalise large-scale LLM and deep learning workloads on Databricks (Mosaic AI, MLflow, Model Serving, Vector Search, Unity Catalog governance), tuned to the fast iteration cycles Emerging DNB customers expect.
• Lead fine-tuning and model-customisation engagements on open LLMs (e.g., Llama-family models), including judge-based and label-efficient evaluation approaches for domains where quality and safety are paramount.
• Act as a bridge to Product and Engineering — channel field and customer feedback into the roadmap and represent the roadmap back to the field.
• Enable and mentor the broader EMEA Emerging DNB Field Engineering team through workshops, reference architectures, and internal enablement, multiplying GenAI expertise across our ~70-person SA/SE organisation.
• Represent Databricks externally as a technical thought leader — conference talks (e.g., Data + AI Summit), blogs, and customer executive briefings.

What We're Looking For

• Strong understanding of the LLM landscape, including leading proprietary and open-source models and providers, with the ability to differentiate their capabilities, trade-offs, and suitability for different use cases, and to articulate a clear, informed point of view (POV) to customers.
• Deep expertise in the modern GenAI stack: LLM application patterns (RAG, agents, tool use), fine-tuning and model customization, prompt and evaluation engineering, and LLM guardrails/safety.
• Strong foundations in machine learning and deep learning, including distributed training, GPU workloads, and the full MLOps lifecycle (tracking, registry, serving, monitoring).
• Proficiency in Python and the ML ecosystem (PyTorch/Transformers, MLflow, Spark), and comfort building production-quality reference implementations.
• Experience with the Databricks platform — or the ability to ramp on it quickly — including Mosaic AI, Model Serving, Vector Search, and Unity Catalog. Cloud experience across AWS, Azure, or GCP.
• Excellent communication and consultative skills: able to earn the trust of both hands-on engineers and senior technical executives, and to explain complex GenAI trade-offs clearly.
• A track record of technical leadership and mentorship — someone who elevates the people around them.
• Comfort operating at the pace of digital-native and high-growth customers, where speed, iteration, and technical credibility win the deal.
• Based in the United Kingdom and able to work across EMEA time zones, with willingness to travel to customer sites within the region as needed.
• Background helping digital-native customers build data and ML solutions (e.g., prior experience at a data/ML platform or consulting vendor) is a strong plus.

Nice to Have

• Public technical presence: conference speaking, published talks, blogs, or open-source contributions in the ML/GenAI space.
• Domain depth in a regulated or high-stakes industry (healthcare/life sciences, financial services) where model quality, evaluation, and safety are critical.
• Experience partnering with Product and Engineering teams to influence roadmap.

Why This Role

About the Role

What You'll Do

• Serve as the deep technical authority on Generative AI, LLMs, and applied machine learning for the EMEA Emerging DNB business unit, supporting the most strategic and complex customer engagements across our digital-native and born-in-the-cloud accounts.
• Partner with our EMEA Emerging DNB Solutions Architects, Solutions Engineers, and Account teams to scope, design, and de-risk GenAI use cases — from retrieval-augmented generation and agentic systems to fine-tuning and evaluation.
• Build hands-on proofs of concept and reference implementations that operationalise large-scale LLM and deep learning workloads on Databricks (Mosaic AI, MLflow, Model Serving, Vector Search, Unity Catalog governance), tuned to the fast iteration cycles Emerging DNB customers expect.
• Lead fine-tuning and model-customisation engagements on open LLMs (e.g., Llama-family models), including judge-based and label-efficient evaluation approaches for domains where quality and safety are paramount.
• Act as a bridge to Product and Engineering — channel field and customer feedback into the roadmap and represent the roadmap back to the field.
• Enable and mentor the broader EMEA Emerging DNB Field Engineering team through workshops, reference architectures, and internal enablement, multiplying GenAI expertise across our ~70-person SA/SE organisation.
• Represent Databricks externally as a technical thought leader — conference talks (e.g., Data + AI Summit), blogs, and customer executive briefings.

What We're Looking For

• Strong understanding of the LLM landscape, including leading proprietary and open-source models and providers, with the ability to differentiate their capabilities, trade-offs, and suitability for different use cases, and to articulate a clear, informed point of view (POV) to customers.
• Deep expertise in the modern GenAI stack: LLM application patterns (RAG, agents, tool use), fine-tuning and model customization, prompt and evaluation engineering, and LLM guardrails/safety.
• Strong foundations in machine learning and deep learning, including distributed training, GPU workloads, and the full MLOps lifecycle (tracking, registry, serving, monitoring).
• Proficiency in Python and the ML ecosystem (PyTorch/Transformers, MLflow, Spark), and comfort building production-quality reference implementations.
• Experience with the Databricks platform — or the ability to ramp on it quickly — including Mosaic AI, Model Serving, Vector Search, and Unity Catalog. Cloud experience across AWS, Azure, or GCP.
• Excellent communication and consultative skills: able to earn the trust of both hands-on engineers and senior technical executives, and to explain complex GenAI trade-offs clearly.
• A track record of technical leadership and mentorship — someone who elevates the people around them.
• Comfort operating at the pace of digital-native and high-growth customers, where speed, iteration, and technical credibility win the deal.
• Based in the United Kingdom and able to work across EMEA time zones, with willingness to travel to customer sites within the region as needed.
• Background helping digital-native customers build data and ML solutions (e.g., prior experience at a data/ML platform or consulting vendor) is a strong plus.

Nice to Have

• Public technical presence: conference speaking, published talks, blogs, or open-source contributions in the ML/GenAI space.
• Domain depth in a regulated or high-stakes industry (healthcare/life sciences, financial services) where model quality, evaluation, and safety are critical.
• Experience partnering with Product and Engineering teams to influence roadmap.

Why This Role