Software Engineer II, Machine Learning

Translate product and business problems into clear machine learning problems with measurable success criteria

Build, train, evaluate, and improve production machine learning models

Partner with software engineers and ML infrastructure engineers to deploy models and improve reliability, scalability, and performance in production

Design and analyze offline evaluations and online experiments to understand model impact

Contribute to feature engineering, data preparation, training pipelines, and model monitoring

Write clean, maintainable, production-quality code and participate in design and code reviews

Communicate technical findings, trade-offs, and recommendations clearly to both technical and non-technical partners

BS or MS in Computer Science, Machine Learning, Statistics, Mathematics, or a related technical field

2+ years of industry experience in machine learning, software engineering, data science, or a related field

Strong foundation in computer science fundamentals, including data structures, algorithms, and software design

Experience building ML or AI-related systems, or strong understanding of how modern machine learning systems are developed and operated

Proficiency in Python and at least one additional programming language such as Java, Kotlin, Go, Scala, or a similar language

Strong understanding of machine learning fundamentals, including model training, evaluation, and experimentation

Strong communication skills and the ability to collaborate effectively across functions

Self-motivated, proactive, and comfortable taking ownership of well-scoped problems

Experience with recommendation systems or casual inference

Familiarity with big data or stream processing frameworks such as Spark or Flink

Familiarity with cloud platforms such as AWS and containerized environments such as Kubernetes

Familiarity with ML model serving frameworks such as TensorFlow Serving, TorchServe, Triton Inference Server, or Ray Serve

Experience with feature stores, ML data pipelines, and orchestration frameworks such as Airflow

Understanding of MLOps practices including CI/CD for ML, model versioning, and automated evaluation

Exposure to observability and monitoring for ML systems

Exposure to LLM-related use cases or applied generative AI projects