Staff ML Engineer (AI)

Grid Dynamics is a leading provider of technology consulting and advanced analytics services. They are seeking an experienced ML Engineer to develop automated judge models and validation frameworks, as well as synthetic data generation pipelines to enhance the evaluation of AI systems.

Responsibilities:

• Train, fine-tune, and validate automated judge models that can reliably score AI system outputs for safety and policy compliance
• Develop calibration and agreement metrics to ensure judges meet human-parity benchmarks
• Design and implement validation frameworks to assess the accuracy, reliability, and cross-linguistic consistency of automated evaluation systems
• Develop methods to detect drift, bias, and failure modes in automated judges across markets
• Develop and maintain synthetic data generation pipelines to augment evaluation coverage, stress-test safety boundaries, and support evaluation in low-resource languages
• Ensure synthetic data is diverse, representative, and validated against human-generated benchmarks
• Create automated pipelines for analysis and reporting that reduce manual effort, increase reproducibility, and enable rapid cross-market safety assessments
• Build tooling that integrates with existing dashboards and reporting workflows

Requirements:

• 3+ years of experience in an ML engineering or applied ML research role, with hands-on experience building and deploying ML models and pipelines
• Strong proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers)
• Experience training, fine-tuning, and evaluating language models and/or classifiers, including prompt engineering and model calibration
• Experience building automated data processing, evaluation, or monitoring pipelines
• Comfortable with experiment design and statistical validation of model performance across segmented samples
• Able to work independently as well as collaboratively with minimal direction
• Organized, highly attentive to detail, and manages time well
• Advanced degree (MS/PhD) in Computer Science, Machine Learning, Natural Language Processing, or a related field
• Experience working in industry
• Experience with synthetic data generation techniques, including data augmentation, paraphrasing, and controlled generation methods
• Experience with multilingual NLP, cross-lingual transfer learning, or low-resource language modeling
• Familiarity with evaluation-as-a-service architectures or automated red teaming frameworks
• Experience with large-scale distributed computing (e.g., Spark, Ray, or cloud-based ML platforms)
• Prior experience in AI safety, responsible AI, content moderation, or trust and safety domains
• Experience with CI/CD integration for ML model validation and deployment