Senior Machine Learning Engineer

Qualifications

• Ph.D., Master’s degree, or equivalent practical experience in Computer Science, Artificial Intelligence, Machine Learning, Operations Research, Statistics, or a related technical field.
• 5+ years of relevant experience with a Master’s degree, or 3+ years with a Ph.D., applying machine learning to real-world problems.
• Strong Python programming skills and experience building production-quality ML, GenAI, or data systems.
• Hands-on experience with PyTorch and modern deep-learning stacks; experience with Hugging Face, LLMs, VLMs, diffusion models, or multimodal models is strongly preferred.
• Experience with data-centric AI or GenAI methods, such as synthetic-data generation, data-quality measurement, dataset curation, weak supervision, model-based labeling, active learning, deduplication, or data augmentation.
• Experience designing experiments and interpreting results through statistical analysis, ablation studies, benchmark evaluation, and error analysis.
• Strong understanding of model training, inference, evaluation, and production monitoring.
• Ability to evaluate research papers, identify practical value, and implement useful techniques in real-world systems.
• Experience building scalable data or ML pipelines using distributed compute, cloud storage, batch processing, or workflow orchestration.
• Strong written and verbal communication skills, including experience preparing technical proposals, design documents, experiment reports, and stakeholder presentations.

Description

• Design and build data-centric Generative AI methods for synthetic data generation, multimodal data curation, augmentation, filtering, deduplication, and data-quality assessment.
• Develop and evaluate synthetic-data pipelines for text, speech, vision, and multimodal GenAI use cases, including controllable generation, provenance tracking, safety checks, and domain adaptation.
• Build evaluation frameworks that connect data quality with downstream model performance through benchmark design, ablation studies, error analysis, and model-feedback loops.
• Research, prototype, and implement modern generative AI techniques, including LLM/VLM-based data generation, fine-tuning, instruction tuning, preference optimization, and model-based data labeling.
• Build scalable data and ML pipelines for data acquisition, cleaning, transformation, metadata extraction, embedding generation, labeling, training, and evaluation.
• Develop production-quality code for batch and real-time ML workflows, including model inference, feature processing, data validation, monitoring, and operational automation.
• Translate research papers and emerging GenAI techniques into practical systems that improve data quality, model performance, and customer-facing AI outcomes.
• Partner with modeling, product, infrastructure, and domain teams to define data requirements, quality standards, evaluation criteria, and delivery plans.
• Operate across the full development lifecycle, including research, prototyping, experimentation, productionization, testing, CI/CD, monitoring, runbooks, and production support.

Responsibilities

Responsibilities

• Lead the design, implementation, and continuous improvement of data-centric GenAI solutions and synthetic-data capabilities.
• Define and maintain data-quality standards, evaluation metrics, and validation processes for GenAI datasets and models.
• Conduct experiments, analyze results, and recommend data or modeling improvements based on measurable outcomes.
• Collaborate with cross-functional teams to prioritize use cases, align on technical requirements, and deliver scalable production solutions.
• Contribute to technical designs, experiment reports, documentation, and stakeholder presentations.
• Support production deployments by establishing monitoring, quality controls, operational processes, and troubleshooting procedures.
• Stay current with relevant research and industry developments, assessing and applying techniques with practical business value.