Top AI & Machine Learning Jobs

The Senior AI Engineer will build and scale AI features, develop AI agents, and partner with product teams to enhance products for small businesses.

Lead the AI research agenda at Kepler, focusing on trustworthy AI for enterprise decisions. Oversee research on agentic systems and evaluation frameworks, and manage a research team. Ensure production deployment of innovative AI solutions based on real financial data.

Build, tune, and QA conversational AI agents on the Regal platform: design prompts and flows, tune STT/LLM/TTS settings, create custom functions, test agents, codify best practices, and provide product feedback and sales demos.

Design and maintain data pipelines and BI infrastructure, ensure data quality, optimize SQL and transformations, build and validate predictive and ML models, support deployment and BI integration, and collaborate with stakeholders to deliver scalable analytics solutions.

Design, train, and implement domain-specific LLMs for automation in restoration companies. Collaborate with teams, coordinate AI engineering, and ensure model reliability.

Lead strategic deployment of Hebbia's AI platform across global financial institutions by applying LLMs to complex legal and banking workflows, advising customers, driving value-backed land-and-expand growth, informing product and commercial strategy, and optimizing post-sales processes to scale adoption and ROI.

Own and improve the AI Social render pipeline and agent loops that generate on-brand Instagram carousels and reels. Build prompts, structured outputs, tool-calling, and media composition to ship high-quality generative image, video, voice, and music products in collaboration with product, design, and content strategy.

Design and deploy production-grade ML systems, integrate models into software pipelines, and develop features leveraging ML for optimization and prediction problems.

Own, build, and optimize Spark-based production data pipelines and ML data lake to support recommender systems and personalization. Scale cluster configurations on GCP Dataproc, ensure data quality and accessibility, support ML engineers for model development, resolve performance bottlenecks, and collaborate on platform architecture to deliver reliable data products that drive business value.

Lead and build an Enterprise AI organization to define strategy, platforms, governance, and standards. Deliver scalable AI platforms (LLM gateways, model registries, vector DBs), enable business adoption, embed responsible AI and compliance, partner across Security, Legal, Finance, and drive measurable AI use cases and ROI while managing a small, cross-functional AI architecture and engineering team.