AI Research Engineer, Model Optimization and Inference

AI Research Engineer, Model Optimization and Inference

Full-Time 60000 - 80000 £ / year (est.) Home office (partial)
Kindredventures

At a Glance

  • Tasks: Architect and build high-performance inference engines for virtual 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 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 the future of interactive entertainment while collaborating with a passionate team in a hybrid work setting in London. With competitive salaries, generous leave, and a culture that values inclusivity and creativity, Iconic is the perfect place for those looking to make a meaningful impact in the world of AI and storytelling.

Kindredventures

Contact Details:

Kindredventures Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land AI Research Engineer, Model Optimization and Inference

Tip Number 1

Network like a pro! Reach out to folks in the industry on LinkedIn or at events. A friendly chat can open doors that a CV just can't.

Tip Number 2

Show off your skills! Create a portfolio or GitHub repo showcasing your projects, especially those related to model optimization and inference. It’s a great way to demonstrate what you can do.

Tip Number 3

Prepare for interviews by brushing up on technical questions and practical scenarios. Think about how you'd tackle real-time challenges in AI and game tech—this is your chance to shine!

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, we love seeing candidates who are proactive!

We think you need these skills to ace AI Research Engineer, Model Optimization and Inference

Model Optimization Techniques
Quantization
Pruning
Distillation
Knowledge Transfer
LLM-specific Inference Optimizations
KV-cache Management

Some tips for your application 🫡

Tailor Your CV:Make sure your CV is tailored to the role of AI Research Engineer. Highlight your experience with model optimization techniques and any relevant projects you've worked on. We want to see how your skills align with our mission!

Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for AI and real-time systems, and explain why you’re excited about working at Iconic. Let us know how you can contribute to our innovative team.

Showcase Your Projects:If you've got any personal or professional projects that demonstrate your skills in model optimization or game tech, don’t hold back! Include links or descriptions in your application so we can see your work in action.

Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen to join our team!

How to prepare for a job interview at Kindredventures

Know Your Models Inside Out

Make sure you’re well-versed in the model optimization techniques mentioned in the job description, like quantization and pruning. Brush up on your knowledge of LLM-specific inference optimizations, as these will likely come up during technical discussions.

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 the intersection of AI and game technology. Research how real-time systems work and be ready to discuss your experience with game engines like Unreal or Unity, as well as any relevant projects you've worked on that showcase your understanding of this area.

Communicate Clearly and Collaboratively

Since collaboration is key in this role, practice articulating your thoughts clearly. Be ready to discuss how you’ve worked with cross-functional teams in the past, especially when it comes to integrating AI models into game environments. Good communication can set you apart!