Lead Software Engineer - AI Data Systems

View full description

Requirements:

• Strong software engineering background with deep experience in building data collection, transformation, and featurization pipelines at scale.
• Proficiency in Python, including async programming and concurrency tools, as well as data-centric frameworks such as Pandas, Spark, or Apache Beam.
• Familiarity with ML model development workflows and infrastructure, including dataset versioning, experiment tracking, and model evaluation.
• Experience deploying and scaling AI systems in cloud environments such as AWS, GCP, or Azure.
• Proven success operating in highly ambiguous environments such as research labs, startups, or fast-paced product teams.
• A track record of working with or alongside high-caliber peers in top engineering teams, research groups, or startup ecosystems.
• Growth mindset, strong communication skills, and a commitment to inclusive collaboration and continuous learning.

Responsibilities:

• Design and implement systems to collect and curate high-quality training datasets for supervised, unsupervised, and reinforcement learning use cases.
• Build scalable featurization and preprocessing pipelines to transform raw data into structured inputs for AI/ML model development.
• Partner with ML engineers and researchers to define data requirements and production workflows that support LLM-based agents and autonomous AI systems.
• Lead the development of infrastructure that enables experimentation, evaluation, and deployment of machine learning models in production environments.
• Support orchestration and real-time inference pipelines using Python and modern cloud-native tools, ensuring low-latency and high availability.
• Mentor engineers and foster a high-performance, collaborative engineering culture grounded in technical excellence and curiosity.
• Drive cross-functional alignment with product, infrastructure, and research stakeholders, ensuring clarity on progress, goals, and architecture.