machine learning engineer

Grid Dynamics is a technology consulting firm providing platform and product engineering, AI, and advanced analytics services to enterprise companies undergoing business transformation.

• Train, fine-tune, and validate automated judge models to 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 to reduce manual effort and increase reproducibility;
• Build tooling that integrates with existing dashboards and reporting workflows.

• 3+ Years of experience in an ML engineering or applied ML research role;
• Hands-on experience building and deploying ML models and pipelines;
• Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers;
• Experience training, fine-tuning, and evaluating language models or classifiers, including prompt engineering and model calibration;
• Experience building automated data processing, evaluation, or monitoring pipelines;
• Proficiency in experiment design and statistical validation of model performance across segmented samples;
• Ability to work independently and collaboratively with minimal direction;
• Strong organizational skills and attention to detail;
• Nice to have: Advanced degree (MS/PhD) in Computer Science, Machine Learning, Natural Language Processing, or related field, industry experience, experience with synthetic data generation techniques, multilingual NLP, cross-lingual transfer learning, low-resource language modeling, evaluation-as-a-service architectures, automated red teaming frameworks, large-scale distributed computing (Spark, Ray), AI safety, responsible AI, content moderation, trust and safety domains, CI/CD integration for ML model validation and deployment.

• Competitive salary;
• Flexible schedule;
• Medical insurance;
• Sports benefits;
• Corporate social events;
• Professional development opportunities;
• Well-equipped office.