ML Applied Scientist

Automated Judge Development: 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.

• Validation Techniques: 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.
• Synthetic Data Generation: 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.
• Scalable Analysis & Reporting Automation: 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.

We are seeking an experienced ML Engineer to join our team to build and scale automated evaluation and synthetic data generation (SDG) capabilities that support safety assessments across languages and markets. You will play a crucial role in training automated judges, developing validation techniques, building automated performance checks, and creating scalable approaches to analysis and reporting. An ideal candidate possesses strong machine learning engineering skills, experience building evaluation and data generation pipelines, and the ability to iteratively and collaboratively with subject matter experts. You will be part of a team who works closely with language experts and multi-lingual annotators to validate automated approaches to safety evaluations, across diverse linguistic contexts., 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.
• 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.
• Advanced degree (MS/PhD) in Computer Science, Machine Learning, Natural Language Processing, or a related field.

Pulled from the full job description

• Health insurance
• Vision insurance
• Dental insurance
• Flexible schedule, * Opportunity to work on cutting-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, vision, dental, etc.
• Corporate social events
• Professional development opportunities
• Well-equipped office

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.