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