We are looking for Lead ML Engineer -LLM Inference & NLP.
Your main tasks will be:
- 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
We expect from you:
- 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
Nice to have:
- 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)
What do we offer:
- REMOTE OPPORTUNITY to work full-time;
- The initial pay level or pay range for this role will be shared with candidates during the recruitment process and before the commencement of employment;
- Vacation 28 calendar days per year;
- 7 wellness days per year (time off) that can be used to deal with household issues, to lie down and recover without taking sick leave;
- Bonuses up to $5000 for recommending successful applicants for positions in the company;
- 50% payment for professional training, international conferences, and meetings;
- Corporate discount for English lessons;
- Health benefits. According to the paychecks, if you are not eligible for corporate medical insurance, the company will compensate you with up to $ 1,000 gross per year per employee. This can be spent on self-purchase of health insurance or on doctor’s fees for yourself and close relatives (spouse, children);
- Workplace organization. The company provides all employees with an equipped workplace and all the necessary equipment (table, armchair, wifi, etc.) in our offices or co-working locations. In the other locations, the company provides reimbursement of workplace costs up to $ 1000 gross once every 3 years, according to the paychecks. This money can be spent on the rent of the co-working room, on equipping the working place at home (desk, chair, Internet, etc.) during those 3 years;
- Internal gamified gratitude system: receive bonuses from colleagues and exchange them for our merchandise, team building activities, massage certificates, etc.
Published on: 10/4/2026
