Tech Lead NLP Engineer

Itcharm; France; Hybrid; EUR 161,000 - 214,000; Full time

Benefits offered by this job

Fully remote, full-time

28 days annual leave

Wellness days (7 per year)

Training & conference budget

English language classes

Health support up to $1000/year

Home office stipend

Referral rewards up to $5000

Job summary

Itcharm is hiring a Lead ML Engineer to own the performance and technical direction of our language models. You will split time between hands-on inference on trillion-parameter models and technical leadership of NLP/CV engineers.

You’ll drive speed and efficiency in production, review experiments, and guide the ML roadmap while staying hands-on and shipping rapidly.

Qualifications

• Hands-on experience optimizing LLM inference in production.
• Experience with distributed inference across multi-GPU clusters.
• Proven ability to lead technical direction in ML teams.

Responsibilities

• Speed up and scale LLM inference in production.
• Lead NLP and CV engineers technically and review experiments.
• Train and fine-tune language models behind AI companions.
• Track cutting-edge research and translate into ML roadmap.

Skills

LLM inference optimization

Distributed inference

SGLang

vLLM

TensorRT-LLM

PyTorch

Transformers

Python

Go

C#

Education

PhD or MSc in CS/Math/Physics

Tools

PyTorch

Transformers

Go

C#

Python

Job description

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:

• 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