Senior Applied ML Engineer

Tasks

• Build multimodal classification and detection systems
• Build training and evaluation datasets
• Collaborate with product engineering legal policy and operations
• Deploy and operate ML models at scale
• Design evaluation frameworks for production
• Develop intellectual property and character recognition
• Enforce content policies for trust and safety
• Establish model monitoring for data drift and quality
• Fine-tune and evaluate vision language models
• Implement NSFW detection
• Measure impact on user experience and business metrics
• Optimize inference pipelines for throughput and latency
• Own applied ML projects end to end
• Run experiments and analyze model performance

Perks/Benefits

• Relocation support

Skills/Tech-stack

Active Learning | Calibration | Class imbalance | Computer Vision | Content Moderation | Cost Sensitive Decision Making | Dataset Building | Decision Making | Fraud Detection | Hard Negative Mining | Inference Optimization | Machine Learning | Model Evaluation | Model retraining | Monitoring | Multimodal Machine Learning | Negative mining | Precisión | PyTorch | Python | Recall | Recommendation Systems | Risk Mitigation | Threshold Tuning | Trust and Safety