Senior Machine Learning Engineer

Description What you’ll do ● Design, develop, and deploy models for classification, prediction, estimation, and behavioral analysis. ● Build AI-powered product capabilities that help users explore data, answer questions, and make informed decisions. ● Adapt and fine-tune LLMs for domain-specific tasks, targeting performance competitive with leading frontier models on well-defined use cases. ● Build datasets, evaluation frameworks, and experiments to measure model quality, reliability, and business impact. ● Develop scalable training and inference pipelines, optimizing serving performance, latency, and cost. ● Operate and monitor models in production, addressing reliability, performance, and model quality over time. ● Identify opportunities to apply AI and machine learning across the product, and turn promising ideas into production capabilities. ● Work closely with product, backend, and data engineering teams to define problems, validate approaches, and deliver measurable results. ● Stay current with advances in ML and generative AI, applying new methods where they meaningfully improve the product. What you bring ● Experience developing, deploying, and operating machine learning models in production. ● Strong Python and SQL skills, with experience working with large, complex datasets. ● A solid foundation in statistics, machine learning, experimental design, and model evaluation. ● Hands-on experience with LLMs, including fine-tuning or adaptation, retrieval, and systematic evaluation. ● Experience with modern ML frameworks and tools such as PyTorch, scikit-learn, or Hugging Face. ● Strong software engineering skills and the ability to build reliable, maintainable ML systems that run in production environments. ● The ability to translate ambiguous business problems into clear modeling objectives and measurable outcomes. ● The ability to independently lead work from problem definition and experimentation through production delivery. ● Ability to move quickly from idea to implementation, using AI tools effectively to accelerate research, experimentation, and development while maintaining engineering rigor and validating results. ● Ownership, curiosity, and a practical approach to balancing research ambition with product delivery. Nice to have ● Experience optimizing model serving, including batching, caching, quantization, or GPU utilization. ● Familiarity with cloud platforms, Docker, Kubernetes, and MLOps practices. ● Experience with Databricks, Spark, or distributed data processing. ● Background in time-series modeling, anomaly detection, or behavioral analytics. ● Familiarity with software engineering workflows, developer tools, or engineering productivity metrics. ● An advanced degree in computer science, statistics, mathematics, or a related field, or equivalent practical experience