ML Engineer

Tasks

• Build scalable training and inference pipelines
• Collaborate with product and analytics teams to define success metrics
• Deploy ML solutions via batch jobs or APIs using Docker and cloud platforms
• Design, develop, and deploy machine learning models
• Develop and refine value prediction signals such as LTV and conversion probability
• Improve recommenders and ranking models using offline and online evaluation techniques
• Own end-to-end ML projects including data exploration modeling deployment and monitoring
• Share knowledge through code reviews documentation and mentoring

Perks/Benefits

• Conference support
• Fully remote
• Health insurance reimbursement
• Language courses discount
• Paid vacation
• Recognition system
• Referral bonuses
• Training reimbursement
• Wellness days
• Workspace support

Skills/Tech-stack

AWS | Azure | Docker | GCP | ML algorithms | Model Deployment | Model Evaluation | Monitoring | NumPy | Pandas | Python | Ranking algorithms | Recommender Systems | SQL | Scikit-learn