Top AI & Machine Learning Jobs

Design and maintain high-fidelity 2D/3D simulation environments, engineer reward functions, implement and optimize RL algorithms (PPO, SAC, Offline RL), coordinate cross-functionally, and develop sim-to-real strategies (domain randomization/adaptation) to ensure autonomous agents perform reliably in real-world multi-sensor settings.

The Director of Analytics & AI Solutions defines and executes product strategy for analytics and AI solutions to aid decision-making across Studio Distribution, collaborating closely with business and engineering teams.

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.

Develop and optimize C++ systems implementing machine learning, computer vision, and (inverse-)procedural 3D modeling algorithms. Collaborate with leadership to translate product vision, manage code with Git, deploy and test on cloud platforms, work with large-scale geospatial datasets, and operate in Unix (bash) environments.

Design and implement high-performance C++ systems for machine learning, computer vision, and 3D/procedural modeling. Apply research, optimize code for speed and scalability, collaborate with leadership, use Git, deploy and test on cloud with large-scale geospatial datasets, and operate in a Unix shell environment.

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.

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.

Lead data curation and validation for production ML: coordinate with ML/product/TPM teams, define data and training specs, oversee pipelines, ensure representative, high-quality behavioral datasets, and translate product goals into actionable data requirements.

Implement deep-learning, computer vision, and inverse-procedural modeling algorithms in Python to convert large 2D/3D and geospatial datasets into high-fidelity 3D content. Apply cutting-edge ML/graphics research, collaborate with founders to define technical milestones, deploy and test code on remote Unix systems, and manage code with Git.