Top AI & Machine Learning Jobs

Lead development of scalable AI-powered systems, rapidly evaluate frontier AI capabilities, and translate them into measurable business value. Build APIs, web frontends, business logic, data layers, and SQL/NoSQL storage. Deploy and troubleshoot cloud applications, create automated tests, maintain code quality, and integrate foundation models, RAG patterns, and agentic AI. Collaborate across multidisciplinary teams to move prototypes into production and deliver solutions in complex, matrixed environments.

Lead and grow a team of ML engineers building applications for a graphically intensive simulation platform. Manage stakeholders, translate customer needs into technical requirements, prioritize between off-the-shelf, open-source, and greenfield solutions, coordinate cross-functional delivery, and oversee the full ML lifecycle from data infrastructure to deployment. Foster team ways of working and performance.

Design high-level systems that integrate ML models into production, enable ML–rendering engine interaction, build language-agnostic APIs/wrappers and microservices, evaluate build-vs-buy options, coordinate cross-functional integration requirements, and decompose product vision into scalable ML-enabled architecture.

Source, aggregate, and manage geospatial datasets (imagery, rasters, vectors, point clouds) and build automated tooling (GDAL, Python, in-house tools) to preprocess and postprocess data for AI/ML pipelines while partnering with AI/ML, pipeline, and product teams.

Source, aggregate, and manage geospatial datasets (imagery, raster, vector, point clouds) for AI/ML use. Build and maintain automated preprocessing and postprocessing tools (using open-source libraries like GDAL/PROJ and Python). Partner with AI/ML, Pipeline, Product teams, and leadership to ensure data coverage, quality, and readiness for model development and deployment.

Design, develop, and optimize LLM-powered applications and autonomous agents. Build agentic workflows with LangGraph, implement prompt engineering, integrate and evaluate LLMs, develop observability capabilities, maintain MCP servers, and deploy backend services using AWS-native technologies. Collaborate cross-functionally, participate in Agile ceremonies, conduct peer reviews, and contribute to engineering standards for reliable, scalable AI systems.

Develop and oversee high-quality datasets and data pipelines for production machine learning. Collaborate with ML engineers, product teams, TPMs, engineering teams, and annotation teams to define training requirements, translate product goals into data specifications, curate and validate behavioral datasets, and ensure data integrity, accuracy, representativeness, and reliability.

Lead data strategy for large 2D and 3D media datasets used in production machine learning. Coordinate with ML, annotation engineering, and technical program management teams; translate project goals into actionable data specifications; oversee dataset curation, filtering, and processing; and ensure data integrity, representativeness, and quality through analysis and inspection.

Lead end-to-end product development for Medallion data products and AI agentic workflows. Prototype and build AI capabilities alongside engineering, manage 2-3 business unit engagements, define roadmaps, prioritize reusable solutions, track adoption metrics, and drive cross-functional standards for data governance and responsible AI.

Design, build, and own scalable MLOps infrastructure for large 2D/3D multimodal datasets. Automate CI/CD and continuous training pipelines, implement dataset versioning and storage strategies, and deploy monitoring/observability systems to track model performance and data drift across production ML systems. Collaborate with ML, annotation teams, and TPMs to define requirements.