🏢 Company overview Unimatch Lab is an AI-driven venture studio based in Silicon Valley. We build our own AI technology stack and a portfolio of AI-driven assets: consumer AI products, smart devices, local LLM clusters, and foundational layers (memory layer, RAM/VRAM optimization, orchestration, AI software for infrastructure and compute). This includes R&D on local, distributed, and orbital data centers and distributed computing. Our goal is to reach a combined asset valuation of $10B+ by 2032 and rank among the world's top 50 AI companies. We are an OKR-driven company: measurable outcomes, speed, transparency, and ownership. We hire A-players: autonomous, fast, strong execution, high accountability. Learn more: Unimatch Lab Career

🎯 Position summary Senior ML Engineer owns end-to-end LLM/VLM lifecycle: datasets, weight training, evaluation, and self-hosted production. The role is weighted toward fine-tuning and serving on our own infrastructure, not prompt assembly, RAG integration, or third-party LLM APIs. Personal and sensitive data are processed only on-premises. External cloud LLM APIs are not used for workloads that include such data. You are expected to run the loop end to end, decide what to retrain from production failures or eval gaps, and keep datasets, eval configs, and release criteria documented so the pipeline is not tied to one engineer.

✅ Key responsibilities

Model training and fine-tuning

• Fine-tune LLM and VLM models for product use cases using LoRA/QLoRA, distillation, and related methods through production-ready weights
• Adapt models to domain-specific tasks without relying on prompt-only or RAG-only approaches as the primary delivery path
• Maintain reproducible training configs and document model versions bound to releases

Evaluation and data

• Build evaluation as a release gate: field-level metrics (exact-match, precision, recall, F1, CER, WER), held-out sets, and train/test leakage checks before rollout
• Own data workflows: collection, teacher-labeling, versioned reproducible datasets, and synthetic data generation with validation
• Define acceptance criteria tied to eval results for production-bound model versions

Production inference and operations

• Deploy and operate self-hosted inference under load: vLLM, batching, VRAM management, quantization, structured and guided JSON outputs
• Monitor production model behavior and initiate retraining from eval failures or production error patterns
• Run containerized deployments (Docker, Kubernetes), CI/CD for model releases, and quality logging for model outputs
• Keep personal and sensitive data within the in-house environment

💪 Required qualifications

• 5+ years of experience delivering ML systems to production, including high-load AI systems
• Hands-on fine-tuning of LLM or VLM models: you train weights (LoRA/QLoRA, distillation)
• OCR on multilingual documents: text and structure extraction from mixed languages within a single file
• Evaluation practice as a release discipline: held-out metrics by field, leakage detection, and clear pass/fail gates before production
• End-to-end data ownership: teacher-labeling, reproducible versioned datasets, validated synthetic data
• Production experience with self-hosted LLM serving (vLLM or equivalent): batching, quantization, structured outputs under load
• Ownership of at least one full cycle to production traffic: data preparation, fine-tuning, evaluation, deployment, and iteration from prod or eval signals
• Strong Python and production backend skills (e.g. FastAPI); PostgreSQL, Redis where applicable
• Docker, Kubernetes, and CI/CD for model and service releases
• In-house / self-hosted ML operation; no sending personal or sensitive data to third-party LLM APIs

⭐ Preferred qualifications

• Experience with Hugging Face Transformers, PEFT, and training tooling in production contexts
• MLOps practice: output quality monitoring, drift signals, model versioning
• Hands-on experience with voice agents and TTS
• STEM degree

🛠️ Technology stack

• Training: PyTorch, Hugging Face Transformers, PEFT (LoRA/QLoRA), distillation workflows
• Serving: vLLM, quantization, guided/structured JSON generation
• Data and eval: held-out splits, field-level metrics, dataset versioning, teacher-labeling pipelines
• Backend: Python, FastAPI; PostgreSQL, Redis
• Infra: Docker, Kubernetes; CI/CD (GitHub Actions, GitLab CI, or similar)
• Observability: logging, model output quality monitoring (Prometheus/Grafana or equivalent)
• Testing: pytest, Pydantic

👥 Team structure You work with product and engineering on architecture and delivery priorities. Engineering partners cover deep backend and shared infrastructure where ownership is split. Your accountability: the ML loop from data through fine-tuning, evaluation, and self-hosted production for LLM/VLM features assigned to this role.

💼 Employment terms

📩 Application process If your background matches this role, please send your resume here.

Parameter	Details
Format	Full-time, remote
Schedule	Flexible hours; sync with the team on CET
Location	Europe: European Union, Albania, Andorra, Bosnia and Herzegovina, United Kingdom, Iceland, Liechtenstein, Moldova, Monaco, Montenegro, North Macedonia, Norway, San Marino, Serbia, Switzerland, Georgia, Armenia, Turkey. LATAM: Mexico, Panama, Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, Paraguay, Uruguay, Venezuela.
Compensation	40-45$ per hour