ML Applied Scientist

Essential functions

• 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.

Qualifications

• 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.
• Strong experimentation background, with the ability to independently research and design experiments to evaluate methods or solutions for specific technical challenges.
• 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.

Preferred Qualifications

• 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.

About us

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.

We offer

• 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

Contact us

To apply, send your resume and cover letter to jobs@griddynamics.com

Equal Opportunity Employer

We are committed to creating an inclusive environment for all employees during their employment and for all candidates during the application process.