Lead ML Engineer - LLM Inference & NLP

• Deep hands-on experience optimizing LLM inference in production with SGLang, vLLM, or TensorRT-LLM
• Experience with distributed inference or training of large models: MoE, tensor/expert/pipeline parallelism, multi-node GPU clusters
• Strong understanding of what makes inference fast: KV cache, attention kernels, batching, quantization, GPU profiling
• Experience training and fine-tuning LLMs, including post-training (RLHF, DPO, or similar)
• Proven technical leadership: you've guided engineers through reviews, mentoring and technical decisions while still writing code yourself
• Proficiency with PyTorch, transformers, and related libraries
• Experience at AI-focused startups or companies (Character AI, OpenAI, and similar is a strong plus)
• Backend engineering experience (Python, Go, C#) and knowledge of scalable deployment systems is a significant advantage
• Advanced English or Russian

• CUDA or Triton kernel development
• A computer vision background. We also welcome strong CV leads who have accelerated large generative image or video models
• Experience with multimodal LLMs
• First-author papers or notable open-source work, e.g. contributions to SGLang, vLLM, or post-training libraries
• A degree in CS, math, or physics from a strong program (MSc or PhD)

We are looking for Lead ML Engineer -LLM Inference & NLP.

• Speed up and scale LLM inference in production: SGLang, KV and prefix caching, batching, quantization, speculative decoding
• Run distributed inference for very large models (up to 1T+ parameters) across multi-GPU and multi-node setups
• Benchmark new GPU servers and hardware, bring them into production and adapt our serving code to them
• Lead the NLP and CV teams technically: review experiments, set direction, and step in early when something is heading the wrong way
• Train and fine-tune the language models, and improve the agent harnesses and chat algorithm that run on them
• Track cutting-edge research and open-source work in inference and post-training, and turn it into the ML roadmap
• Collaborate closely with the validation, content, and dataset preparation teams to design experiments and measure model quality