Design and implement Gen AI solutions and partner closely with ML Engineering, Personalization, Forecasting, and Decision Science domains.
Work cross-functionally with various data science teams, data engineering teams, and data architecture teams.
Be a hands-on technical leader skilled as an implementation engineer while also leading the team to innovate and build cutting-edge Gen AI products and services.
Analyze model behavior, diagnose failure modes, and iterate on data, features, and architectures to improve performance and robustness.
Document modeling assumptions, evaluation results, and trade-offs to support reproducibility and cross-team collaboration.

Requirements

Master’s or Ph.D. in Computer Science, Data Science, Statistics, Applied Mathematics, Machine Learning, or a closely related field.
Strong theoretical and practical foundation in: o Supervised and unsupervised learning o Probability and statistics o Optimization and linear algebra
Proficiency in Python for data analysis, feature engineering, and model development.
Hands-on experience with at least one ML framework such as PyTorch, TensorFlow, or scikit-learn.
Experience working with SQL and large-scale datasets.
Familiarity with model evaluation, error analysis, and experiment design.
Academic, internship, or project experience in one or more of the following: o Search relevance and ranking models o Information retrieval (lexical and semantic) o Recommender systems (collaborative filtering, content-based, hybrid) o Agentic implementations preferably user-facing
Exposure to deep learning architectures (e.g., transformers, bi-encoders and cross-encoders).
Understanding of offline and online evaluation methodologies, including A/B testing.
Experience using cloud or distributed computing environments (e.g., AWS, Spark, Ray) through coursework or projects.
Familiarity with model deployment concepts such as batch inference, real-time APIs, and model monitoring (conceptual understanding sufficient).
Strong analytical thinking and ability to translate ambiguous product problems into well-defined modeling tasks.

Benefits

Annual allotment of free hotel stays at Hyatt hotels globally
Flexible work schedules
Work-life benefits including well-being initiatives such as a complimentary Headspace subscription, and a discount at the on-site fitness center
A global family assistance policy with paid time off following the birth or adoption of a child as well as financial assistance for adoption
Paid Time Off, Medical, Dental, Vision, 401K with company match

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

PythonSupervised learningUnsupervised learningProbabilityStatisticsOptimizationLinear algebraMachine learning frameworksModel evaluationError analysis

Soft skills

Analytical thinkingTechnical leadershipCross-team collaborationProblem translation

Certifications

Master’s degreePh.D.