Senior Machine Learning Engineer

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