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EverAI Limited ищет специалиста по обучению и улучшению LLM для потребительского продукта с миллионами пользователей. Вам предстоит развивать модели, управлять данными и оценками, доводить эксперименты до продакшн‑уровня, работать над персонализацией, согласованностью поведения и мультиязычностью на глобальном рынке.
Команда полностью дистанционная, гибкая локация и возможность сотрудничества с CTO, командой продукта и DevOps.
Our Vision & Products EverAI — Building the Future of AI Companionship One of the Top 15 Largest & Fastest-Growing AI Companies in the World 50 Million Users in 2 years — Help Us Reach 100M first, 500M next At EverAI, we’re shaping what it means to connect with AI.
At EverAI, we’re shaping what it means to connect with AI. With 50 million users and counting, we’re not just building products — we’re creating entirely new categories. Our flagship product is the world’s largest AI companionship platform, redefining relationships for millions. It is governed by our proprietary moderation system, EverGuard — an internal AI designed to ensure everything we build is safe, ethical, and human-first. And we’re only just getting started!
Our Team We are an enthusiastic, passionate and hardworking team of ≈ 100 people. Our founding team has strong entrepreneurial experience building and scaling web products from 0 to IPO.
Alexis Soulopoulos [CEO] - 10+ years in Tech Executive Leadership - Co-Founder Mad Paws Holdings (from 0 to IPO) - Forbes 30 under 30 + Deloitte TechFast50 ’22 & ‘23
Michael Monin [Co-founder & CTO] - 10+ years as CTO / COO (web2/web3), 3 years in AI/LLM - Serial-entrepreneur: MTK Digital (exited / 0->$20m revenue) and Zipchat (AI Chatbot for E-commerce brands)
Thomas Lacroix [Co-founder & CMO] - 8+ years in Customer Acquisition & E-commerce Growth - Serial-entrepreneur: Curatible (sold to Blackstone) and MTK Digital (exited / 0->$20m revenue)
Most LLM roles stop at experimentation. Here, the models you improve power experiences for 50M+ users. Most of our traffic runs on self-hosted, fine-tuned open-weight models. On our internal roleplay evals, our smaller fine-tuned models can outperform much larger off-the-shelf models. Your job is to make them better. You’ll join a small AI team led by Marco, our LLM Tech Lead, working closely with our CTO, Product, Web and DevOps teams. You’ll work across data, post-training, evals, and model behavior, taking ideas from failure analysis to production. The problems are real: personalization, character consistency, long conversations, better evals, multilingual behavior, and faster training cycles. If you want to shape model behavior at consumer scale, this is the role. Fine-tune open-weight LLMs using SFT, LoRA/QLoRA and post-training methods such as DPO and GRPO Diagnose model failures and turn them into experiments. Decide whether the answer is better data, post-training, prompting or classifiers Build training data and evals that directly shape what gets trained next Improve model behavior across personalization, consistency, tone, multilingual and group interactions Ship to production and use real user behavior to guide the next iteration Speed up the training loop so experiments are faster, easier to compare, and more reproducible