At a Glance
- Tasks: Architect and build high-performance inference engines for digital actors in real-time entertainment.
- Company: Join Iconic, a pioneering company at the forefront of AI and interactive entertainment.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Be part of a small, dynamic team with significant autonomy and career advancement opportunities.
- Why this job: Make a real impact on the future of gaming with cutting-edge AI technology.
- Qualifications: MSc or PhD in Computer Science or related field, with strong model optimization experience.
The predicted salary is between 60000 - 80000 £ per year.
At Iconic, our virtual actors don't just generate “text” or “actions”—they perform. They need to speak, move, and perceive in milliseconds, often running locally on a player's machine alongside a rendering engine. You will bridge the gap between massive research models and the constraints of real‑time interactive entertainment.
You will architect and build the inference engine that powers our digital entities. Your main task will be tearing apart the model architecture to make it run as fast as possible on consumer hardware while keeping their abilities intact for the intended usage.
As part of a small, focused team, you'll have significant autonomy and end‑to‑end ownership. You will work at the intersection of System ML and Game Tech. You might spend one day implementing a custom pruning algorithm for our TTS model, and the next day writing a C++ wrapper to expose that model to our game engine. You will work closely with our Character Research team to ensure that optimization never comes at the cost of the character's soul.
Key Responsibilities- Architect Low‑Latency Runtimes: Build and maintain high‑performance inference pipelines for Multimodal LLMs, TTS, and Vision models, targeting both server‑side (H100/A100) and consumer edge (RTX 5090, Apple Silicon) environments.
- State‑of‑the‑Art Optimization: Implement advanced techniques like Speculative Decoding, KV‑Cache quantization, PagedAttention, and Layer Pruning to minimize Time‑To‑First‑Token (TTFT) and Time‑Per‑Output‑Token (TPOT), maximizing throughput.
- Model Compression: Lead our efforts in post‑training quantization (AWQ, GPTQ, GGUF) and distillation to fit massive models into consumer VRAM budgets.
- Engine Integration: Collaborate with the game engineering team to ensure thread‑safe, non‑blocking asynchronous inference within the game loop.
- Custom Kernel Development: Write custom ops in CUDA, Triton, or Metal when off‑the‑shelf kernels aren't fast enough.
- MSc or PhD in Computer Science, Machine Learning, or a related field (or equivalent industry experience).
- Strong experience with model optimization techniques (quantization, pruning, distillation, knowledge).
AI Research Engineer, Model Optimization and Inference in London employer: Iconic Interactive
At Iconic, we pride ourselves on fostering a dynamic and innovative work culture where creativity meets cutting-edge technology. As an AI Research Engineer, you'll enjoy significant autonomy and the opportunity to work on groundbreaking projects in a collaborative environment, all while being supported by a team that values your growth and contributions. Located in a vibrant tech hub, we offer competitive benefits, continuous learning opportunities, and the chance to be at the forefront of real-time interactive entertainment.
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We think this is how you could land AI Research Engineer, Model Optimization and Inference in London
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We think you need these skills to ace AI Research Engineer, Model Optimization and Inference in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Iconic Interactive, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Iconic Interactive. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Iconic Interactive
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Iconic Interactive!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
