WHAT YOU’LL DO- Solve business-critical problems using a mix of classical ML, deep learning, and generative AI.
- Collaborate with product, science, and engineering teams to execute on the technical vision and roadmap for Applied AI initiatives.
- Deliver high-quality, production-ready ML systems and infrastructure, from experimentation through deployment and monitoring.
- Adopt best practices in ML development lifecycle (e.g., data versioning, model training, evaluation, monitoring, responsible AI).
- Deliver enduring value in the form of software and model artifacts.
BASIC QUALIFICATIONS- Master or PhD or equivalent experience in Computer Science, Engineering, Mathematics or a related field and 2 years of Software Engineering work experience, or 5 years Software Engineering work experience.
- Experience in programming with a language such as Python, C, C++, Java, or Go.
- Experience with ML packages such as Tensorflow, PyTorch, JAX, and Scikit-Learn.
- Experience with SQL and database systems such as Hive, Kafka, and Cassandra.
- Experience in the development, training, productionization and monitoring of ML solutions at scale.
- Strong desire for continuous learning and professional growth, coupled with a commitment to developing best-in-class systems.
- Excellent problem-solving and analytical abilities.
- Proven ability to collaborate effectively as a team player.
PREFERRED QUALIFICATIONS- Prior experience working with generative AI (e.g., LLMs, diffusion models) and integrating such technologies into end-user products.
- Experience in modern deep learning architectures and probabilistic models.
- Machine Learning, Computer Science, Statistics, or a related field with research or applied focus on large-scale ML systems.
* Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to .