We are a platform for AI-lationships — personalized, emotionally intelligent connections between people and AI characters. We build characters that have their own personality, mood, and voice: they can ignore you, argue with you, miss you. If you've seen the film Her — that's roughly where we're headed.

Our platform serves both emotional and intimate needs without judgment, built on the values of rejection-free connection, sex positivity, freedom to be yourself, and ethical integrity. Today we have tens of millions of conversations on the platform. Our goal: 10% of the global population in long-term AI-lationships with our characters.

We build open-source generative models, agent architectures, and training infrastructure. All code, weights, and data are public. We're not chasing the next ChatGPT wrapper; we're working on true AI agency: persistent memory, self-modification, self-authored constraints.

We're looking for a Lead ML Engineer to own the performance and technical direction of our language models. About two-thirds of your time goes into hands-on inference work: making very large models, up to a trillion parameters and beyond, run faster and cheaper in production on distributed GPU infrastructure. The rest goes into technical leadership of our NLP and CV engineers.

You lead through expertise, not project management. You review experiments, set technical direction and catch wrong turns early, while deadlines and delivery tracking stay with others. This is a hands-on role. We move fast and expect you to go from idea to deployed result in days, not months. As the team grows, there is a clear path to Head of ML.

WHAT YOU’LL DO

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 behind our AI companions, 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

WHAT WE’RE LOOKING FOR

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 ([ANON], [ANON], 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)

WHY JOIN

You're joining the company that's redefining what relationships with AI look like — before the category gets crowded
We move fast: ideas become live experiments in days, not quarters
We gives you access to frontier model research — open-source, no corporate gatekeeping
Small team, high trust, high ownership — you'll see the direct impact of your work
Work from anywhere with our fully remote, full-time setup.
Standard 28-day annual leave policy.
7 yearly wellness days for life admin or recovery—no sick notes needed.
Referral rewards up to $5000 for helping us hire top talent.
We cover 50% of costs for training, conferences, and global meetups.
Subsidized English classes through our corporate discount.
Health support: receive up to $1,000 annually for medical fees or private insurance if you aren't on the group plan.
Optimized workspace: we equip you in-office or provide $1000 every three years to perfect your home office or co-working setup.
Peer-to-peer recognition: earn gratitude bonuses and swap them for swag, massage vouchers, or team adventures.

NEXT STEPS

Intro call — conversation with the [ANON], includes a short live technical quiz (5 questions, camera on — no AI tools)
Technical interview — live coding session with AI tools allowed, plus ML and ML system design questions with [ANON] and [ANON] (90 min)
Final interview — culture fit and team alignment