Role Description We’re looking for a highly skilled, independent, and driven Machine Learning Engineer to lead the design and development of our next-generation real-time inference services - the core engine powering algorithmic decision-making at scale. This is a rare opportunity to own the system at the heart of our product, serving billions of daily requests across mobile apps, with tight latency and performance constraints. - Own and lead the design and development of low-latency Algo inference services handling billions of requests per day. - Build and scale robust real-time decision-making engines, integrating ML models with business logic under strict SLAs. - Collaborate closely with DS to deploy models seamlessly and reliably in production. - Design systems for model versioning, shadowing, and A/B testing at runtime. - Ensure high availability, scalability, and observability of production systems. - Continuously optimize latency, throughput, and cost-efficiency using modern tooling and techniques. - Work independently while interfacing with cross-functional stakeholders from Algo, Infra, Product, Engineering, BA & Business. Qualifications - B.Sc. or M.Sc. in Computer Science, Software Engineering, or a related technical discipline. - 5+ years of experience building high-performance backend or ML inference systems. - Deep expertise in Python and experience with low-latency APIs and real-time serving frameworks (e.g., FastAPI, Triton Inference Server, TorchServe, BentoML). - Experience with scalable service architecture, message queues (Kafka, Pub/Sub), and async processing. - Strong understanding of model deployment practices, online/offline feature parity, and real-time monitoring. - Experience in cloud environments (AWS, GCP, or OCI) and container orchestration (Kubernetes). - Experience working with in-memory and NoSQL databases (e.g. Aerospike, Redis, Bigtable) to support ultra-fast data access in production-grade ML services. - Familiarity with observability stacks (Prometheus, Grafana, OpenTelemetry) and best practices for alerting and diagnostics. - A strong sense of ownership and the ability to drive solutions end-to-end. - Passion for performance, clean architecture, and impactful systems. Benefits - Polish public holidays. - 20 working days per year is Non-Operational Allowance and settled to be used for personal recreation matters and are compensated in full. These days have to be used within the year, with no rollover to the next calendar year. - Health Insurance. - Gym Subscription (Multisport). Company Description A leading mobile marketing and audience platform, empowers the app ecosystem with cutting-edge solutions in mobile marketing, audience building, and monetization. With integration into over 500,000 monthly active apps and a global reach, the platform leverages first-party data to deliver impactful and scalable advertising solutions.

• The Lead AI Engineer designs and delivers AI and computer vision solutions on Microsoft Azure, focusing on scalable, production‑ready systems. • This role bridges data science and engineering and owns AI components end‑to‑end.

About the role You’ll own and expand our AI-enabled plan ingestion pipeline—from raw PDFs to clean, dependable outputs that power takeoff, design, and downstream automation. What you'll do - Architect, build, and deploy ML systems for object detection, segmentation, document understanding, and information extraction from complex building plans - Drive R&D across computer vision and/or NLP as applied to construction documents and related data - Develop multi-modal approaches that combine vision with language features for document analysis (LLMs/NLP). Familiarity with different RAG methods is a plus - Mentor junior engineers and help raise the bar on execution, code quality, and decision-making - Ensure systems are reliable, maintainable, testable, and observable in production Qualifications - 8+ years of experience in Machine Learning Engineering, with deep focus in computer vision - Expert Python skills, with strong experience in PyTorch and FastAPI - Proven work with segmentation models, detection frameworks, and OCR/document pipelines - Strong image processing fundamentals (OpenCV, geometric transforms, preprocessing, etc.) - Comfortable in Linux; hands-on with Docker/containerization for deployment - Experience building and maintaining MLops infrastructure (e.g., ClearML, MLflow) - Ability to design scalable, high-performance ML systems (not just prototypes) - Strong problem-solving ability and comfort operating independently in a fast-paced environment What Makes You a Great Fit - High agency: you ship, iterate, and fix what’s broken—without waiting for permission - You’ve delivered ML systems end-to-end in production (data → model → deployment → iteration) - You’re a strong engineer who cares about getting better—trajectory matters here - You communicate clearly, especially with a remote and cross-cultural team - You’re gritty and genuinely enjoy solving hard problems Bonus Points - Experience building systems for document reading / understanding - Ambitious, driven, and opinionated about quality and craft - Experience with deep learning inference frameworks (e.g., TensorRT, PyTorch compile) and optimizing inference performance - You keep up with current ML/AI developments and can apply them pragmatically What We Offer - Competitive salary + meaningful equity - Comprehensive benefits (health, dental, vision) - Professional development budget (courses, conferences, research exploration) - Real-world ML problems with direct impact - High growth potential

• Design and implement ML algorithms to enhance music creation tools and solve various user problems in line with product goals • Identify and implement off-the-shelf ML and AI tools to solve practical problems efficiently • Understand the requirements of running models in production, including domain shift testing, QA, A/B testing and so on • Maintain production-ready code with considerations for how solutions fit the product and enhance the user experience • Build scalable, maintainable data pipelines to handle audio and other unstructured data • Collaborate with Product and Engineering teams to ensure seamless integration of ML solutions into production systems • Evaluate, deploy, and fine-tune pre-trained models for tasks like audio analysis, melody generation, and process automation • Uphold ethical AI practices, ensuring fairness and responsible AI use in music-related applications