At a Glance
- Tasks: Architect and build high-performance inference engines for digital actors in real-time entertainment.
- Company: Join Iconic Interactive, a pioneering startup at the forefront of AI and storytelling.
- Benefits: Competitive salary, equity, 25 days leave, private healthcare, and hybrid work options.
- Other info: Inclusive culture with opportunities for growth and collaboration on cutting-edge technology.
- Why this job: Be part of a team innovating at the intersection of AI, art, and immersive experiences.
- Qualifications: MSc or PhD in Computer Science or related field, with strong model optimization experience.
The predicted salary is between 60000 - 80000 £ per year.
The Mission
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.
The Role
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.
Requirements
- 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 transfer).
- Experience with LLM-specific inference optimizations (KV-cache management, speculative decoding, attention mechanisms).
- Proficiency in C/C++.
- Hands-on experience deploying ML models on-device or in latency-sensitive environments.
- Proficiency in Python and deep learning frameworks (PyTorch, JAX, or TensorFlow).
- Experience with inference optimization tools and runtimes (TensorRT, ONNX Runtime, Core ML, or similar).
- Strong systems and engineering skills.
- Excellent collaboration and communication skills.
Nice to Have
- Experience with On-Device AI stacks: ExecuTorch, CoreML, MLX, or ONNX Runtime.
- Experience in CUDA programming.
- Familiarity with non-NVIDIA compute (AMD/ROCm, DirectML, Vulkan Compute).
- Background in real-time systems or game engines (Unreal, Unity) or Real-Time Rendering.
- Publications or demonstrated work in efficient ML or model compression (NeurIPS, ICML, MLSys, etc.) or open-source contributions to projects like vLLM, SGLang, llama.cpp, or bitsandbytes.
Why Join Us
Be a foundational member of a team innovating at the intersection of AI, art, and storytelling. You'll help shape the research direction, culture, and technical foundations of a company building toward something genuinely new.
What we offer
- Competitive salary and equity compensation.
- 25 days annual leave + bank holidays.
- Private healthcare.
- Based in London with hybrid work.
- Inclusive & friendly company culture with socials and game breaks.
About Iconic
Iconic Interactive is a seed-stage startup building AI that breathes life into virtual worlds. The future of entertainment is personal: entire universes shaped around each of us, where you are not watching a story but living at the center of one, shaping it. We're building every layer of intelligence these experiences need: characters that feel and convey meaning, narrators that weave your story, and world directors that act like an ever-present game master: adapting, orchestrating, surprising. We're a growing team tackling some of the most fascinating problems in AI: creating minds that inhabit and shape new worlds.
AI Research Engineer, Model Optimization and Inference in London employer: Kindredventures
At Iconic, we pride ourselves on being an exceptional employer, offering a dynamic work environment where innovation thrives. As an AI Research Engineer, you'll enjoy significant autonomy and the opportunity to shape cutting-edge technology in a collaborative, inclusive culture. With competitive salaries, equity compensation, and a focus on employee growth, our London-based team is dedicated to pushing the boundaries of AI in entertainment while ensuring a healthy work-life balance.
StudySmarter Expert Advice🤫
We think this is how you could land AI Research Engineer, Model Optimization and Inference in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to model optimization and inference. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice coding challenges and be ready to discuss your past experiences with model compression and real-time systems.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team at Iconic.
We think you need these skills to ace AI Research Engineer, Model Optimization and Inference in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the AI Research Engineer role. Highlight your expertise in model optimization techniques and any relevant projects you've worked on, especially those involving real-time systems or game tech.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about AI and how your background makes you a perfect fit for our team. Share specific examples of your work in model compression or inference optimizations to really stand out.
Showcase Your Technical Skills:Don’t shy away from detailing your proficiency in C/C++, Python, and deep learning frameworks. If you've got experience with tools like TensorRT or ONNX Runtime, make sure to mention it—this is your chance to shine!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to see your application and get to know you better. Plus, it shows you're genuinely interested in joining our team at Iconic!
How to prepare for a job interview at Kindredventures
✨Know Your Models Inside Out
Make sure you’re well-versed in the latest model optimization techniques like quantization and pruning. Brush up on your understanding of LLM-specific inference optimizations, as these will likely come up during your interview.
✨Showcase Your Coding Skills
Be prepared to demonstrate your proficiency in C/C++ and Python. You might be asked to solve a coding problem or discuss your previous projects, so have examples ready that highlight your experience with deep learning frameworks and custom kernel development.
✨Understand the Game Tech Landscape
Familiarise yourself with game engines like Unreal or Unity, and be ready to discuss how you can integrate AI models into these environments. Knowing about real-time systems and how they interact with AI will give you an edge.
✨Communicate Clearly and Collaboratively
Since you'll be working closely with a small team, strong communication skills are key. Practice explaining complex concepts in simple terms, and be ready to discuss how you’ve collaborated with others in past projects.