Machine Learning Engineer working with Algorithm team on customer onboarding processes. Focus on execution and automation of models using computer vision and AI in sports industry.
Responsibilities
Customer Onboarding: Execution and Automation (Core Focus)
End-to-End Execution: Execute the complete new customer onboarding workflow for both projects, from initial data readiness to final deployment.
Data Preparation: Managing the 'man in the loop' steps to prepare and validate new customer-specific data (logos, entities) for training.
Model Training & Retraining: Executing and monitoring existing training pipelines for the YOLO-based Logo Detection system, and initiating training for the Smart Linkage components.
Evaluation: Running and analyzing standard evaluation procedures and metrics to ensure models meet customer-specific performance benchmarks before deployment.
Configuration & Deployment: Setting specific configuration files for features like Graphics Brands Types locations and entity mapping, and managing the final model deployment.
Pipeline Optimization: Systematically identify bottlenecks and manual steps within the current onboarding process and engineer solutions to reduce "man in the loop" time.
Internal Tooling & Infrastructure Improvement: Design and implement scalable internal tools and scripts to simplify and automate repetitive tasks across the evaluation and data preparation stages.
Data Flow Collaboration and Support (Supporting Duty): Collaboration with Tagging Team, assist in prioritizing labeling tasks and performing data quality checks.
Requirements
Education: Degree in Computer Science, Electrical Engineering, or a related field (focus on AI/ML/CV)
Python Proficiency: Expert-level Python skills with a commitment to writing clean, modular, and maintainable code
ML Frameworks: Hands-on experience with PyTorch, TensorFlow, or scikit-learn
Production Mindset: Solid understanding of the full ML lifecycle (preprocessing, training, evaluation, and deployment)
Experimentation: Experience running, analyzing, and documenting ML experiments to identify optimal approaches
Excellent team player, dependable, and results-oriented
Benefits
Competitive salary range
Medical insurance
Paid vacation and sick leaves
MultiSport card
Top equipment kit, co-workings
Hybrid set of works (Office location: Warsaw)
Collaborative and innovative work environment
Career growth and development opportunities
A chance to work with giants of the sports industry
Senior Machine Learning Engineer at Pivotal Health developing ML systems for healthcare reimbursement. Collaborating across teams to build and maintain reliable, production - grade machine learning systems.
Senior Machine Learning Engineer at Troveo designing and optimizing machine learning pipelines for AI video models. Collaborating with cross - functional teams to build scalable video data solutions.
Software Engineer focusing on ML infrastructure for drug discovery at Genesis AI. Leading engineering efforts to enhance scalable platforms for generative modeling and large - scale simulations.
AI/ML Engineer developing machine learning systems for TymeX's digital banking platform. Collaborating across teams to enhance customer interaction and personalization through AI technology.
Lead Machine Learning Engineering at Foundation Health, designing and deploying production ML systems. Collaborate with teams to implement AI/ML solutions for impactful healthcare.
Staff ML Engineer at GEICO leading the design and deployment of AI applications. Collaborating with teams to ensure scalability and reliability of generative AI solutions.
Senior Staff ML Engineer serving as a technical leader for Generative AI applications at GEICO. Collaborating with engineers to design and develop high - performance AI solutions.
Senior Machine Learning Engineer working with Adobe's Generative AI Services team. Designing scalable AI systems and optimizing GPU pipelines for Adobe's suite of products.