ML engineer in health-tech

Компания, занимающаяся разработкой облачных технологий для здравоохранения на базе искусственного интеллекта, ищет талантливого Senior ML инженера для долгосрочного сотрудничества. Вы присоединитесь к международной команде первоклассных профессионалов, которые с энтузиазмом создают продукты, улучшающие качество медицинских услуг.

• Build and operate ML solutions for multimodal healthcare data processing and real-time AI agent serving.
• Clinical document analysis: extract, classify, and structure medical data
• Medical text processing: summarize clinical notes, provide clinical decision support
• Conversational AI: develop voice and chat agents for healthcare workflows
• Architect, implement and optimize ML infrastructure using GCP tools (Vertex AI, BigQuery, Cloud Composer, GKE, Pub/Sub)
• Design and develop DAG-based batch pipelines and real-time API endpoints for ML model and multimodal agent serving
• Deploy and maintain self-hosted LLM solutions with custom fine-tuning and production optimization
• Build monitoring and observability for production systems: track system health, model performance, inference latency and operational costs
• Drive technical decisions and collaborate with cross-functional teams to integrate ML solutions into healthcare business processes

• 5+ years as an ML Engineer building, deploying and running ML solutions in high-load, real-world business processes
• Proven track record of deployment of high-load ML solutions into production from R&D and Prototyping to Deployment and Productivization
• Proven track record of building monitoring systems for ML solutions
• Cloud-Native Experience with GCP – 3+ years using ML/data products (Vertex, BigQuery, Cloud Composer, GKE, Pub/Sub)
• This role requires working in a Unix-like Development Environment (e.g., macOS, Linux). We do not use Windows-based workstations for Engineering or AI-related tasks. Virtualization (e.g., WSL) isn’t enough.
• We expect strong productization skills — the ability to take ML and LLM solutions beyond prototypes and deliver production-ready systems with a focus on latency, reliability, observability, cost efficiency, and continuous operation in healthcare environments.
• Experience with high-performance LLM inference engines such as vLLM (or similar)
• Strong Python expertise is required for production-grade ML/NLP/LLM development, including pandas, asyncio-based services, FastAPI, and Pydantic.
• A solid understanding of data architecture and database internals is important — schema design, DDL, partitioning, indexing, storage formats, and cost/performance optimization at scale.
• We also require strong SQL skills for working with large BigQuery datasets, including clustering, partitioning, and query optimization.
• Deep experience with modern ML and NLP is essential — PyTorch, Transformers, the Hugging Face ecosystem, multimodal models and retrieval-augmented methods.
• Upper-Intermediate English or higher; ability to present work and lead discussions with US-based teammates, customers, and stakeholders.
• Будет плюсом: Cloud-Native Experience with GCP – 3+ years using ML/data products (Vertex, BigQuery, Cloud Composer, GKE, Pub/Sub); Кандидаты с высоким уровнем владения GCP всегда будут иметь приоритет перед Azure, AWS и другими облачными платформами

• The company has Unlimited PTOs Policy and compensated New Years Holidays on top of that. The misuse of the policy isn’t welcomed, though it’s definitely possible to take at least two weeks – and fully compensated – vacation, or more.
• The company provides Corporate Hardware for employees who completed their Probationary Period, as well as proven their value.
• We use Google Workspace, Slack, Zoom, and similar collaboration tools for messaging, meetings, and document sharing across the organization. We don’t use Microsoft Outlook, Microsoft Team and similar software.
• Relocation assistance to a desired country may be provided after the probationary period, based on business needs and demonstrated performance.
• The company partially compensates Team Building events, when multiple teammates are located in nearby countries.