Machine Learning Lead en España

• Technical degree and a real ML engineering background - you have trained and shipped models yourself, not only managed people who do.
• Experience leading a team of 3-5 ML engineers, including hiring and performance decisions.
• Expert-level Python, solid understanding of transformer architecture and modern LLM behavior, hands-on with training, fine-tuning and evaluation.
• Production experience with LLM systems: APIs, streaming, batching, fallbacks, cost/latency trade-offs, observability over traces and transcripts.
• Ability to read fresh research and turn it into a prototype, an eval and a shipped change with measurable impact.

Nice to have: experience beyond text (computer vision, image generation, multimodal), long-running conversations and character consistency, vLLM / TGI / SGLang, DeepSpeed / FSDP / Accelerate, quantization, safety classifiers.

• Fluent Russian, ready to work in CET (±2) hours.

• 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.

Sounds good? Join us now!

Social Discovery Group (SDG) is a group of social discovery companies. SDG solves the problems of loneliness, isolation, and disconnection - transforming virtual intimacy into the new normal. SDG's products redefine the way people interact and connect with one another.

Our portfolio includes social entertainment platforms designed to connect people online across different cultures and regions of the world.

We bring together a team of like-minded people and IT professionals who specialize in creating and developing globally impactful social discovery products. Our international team of digital nomads works remotely from all over the world.

We're proud to be a two-time "Great Place to Work" winner (USA & Japan, 2024-2025) and a Top-5 Company for Work-From-Anywhere Jobs (FlexJobs, 2025).

We are looking for Machine Learning Lead.

• Own the ML strategy for dialogue systems: decide what to build and in what order, and tie those bets to business metrics (ARPU, retention, chat depth).
• Lead a team of 3 ML engineers - set the technical bar, distribute work, hire and let go, grow the people you keep.
• Drive LLM post-training and model adaptation end to end: SFT, LoRA/QLoRA, preference optimization (DPO / ORPO / SimPO / GRPO), dataset construction.
• Design agent harnesses and tool-using LLM systems: tool calling, structured outputs, routing, retries, memory and context, guardrails.
• Build the evaluation layer: offline and pairwise evals, LLM-as-a-judge, regression suites that actually correlate with A/B outcomes.
• Cut dialogue failure modes - loops, contradictions, persona drift, context loss, generic replies - and keep inference efficient on latency and cost per message.
• Stay hands-on where it matters (roughly 10-20% of your time): prototypes, debugging agent traces, reviewing your team's work.