Top AI & Machine Learning Jobs

Lead AI transformation for North America: define roadmap, identify and prioritize use cases, drive end-to-end implementation (including hands-on build and deployment), track KPIs/ROI, manage stakeholders and partners, and support organization-wide adoption and training.

The AI Developer will design, implement, and deploy AI agents and workflows, integrating advanced models and ensuring reliable performance in production environments.

Lead customer-facing delivery of data and AI solutions: design, build, deploy, and maintain data pipelines and infrastructure, optimize data quality, collaborate in cross-functional agile teams, and support pre-sales and technical advisory to drive GenAI and Foundry-based implementations.

Own generative AI discovery, scaling, and maintenance for a portfolio of AI characters. Select and standardize models, build pipelines for face consistency and video, train and debug LoRAs, control GPU costs, design prompt architectures, define evaluation metrics and quality gates, and translate creative briefs into technical specs while testing frontier models and tooling to scale the studio.

Consult on predictive analytics projects: build, validate and deploy predictive models; extract and combine data from multiple sources; develop prototypes and recommendation engines; communicate value to clients; support internal initiatives and client engagements across retail, healthcare, and finance. Travel ~30%.

Develop and apply math, statistics, and machine learning techniques to identify patterns and predict signals in global derivatives markets. Collaborate with traders, engineers, and researchers to build fair value models and quantitative trading strategies.

Hybrid technical/customer-facing role building and refining AI risk-assessment modules using LLMs, writing SQL, and creating Power BI dashboards. Lead client onboarding, translate safety programs into the platform, partner on scoping and SOWs, serve as primary technical contact, QA AI outputs, and drive product improvements from client feedback.

Implement and scale research ideas into large-scale training systems: run experiments on GPU clusters, build data and training infrastructure, process massive multimodal datasets, improve observability, and bridge research code with production systems for robotics and multimodal model development.

Lead full-stack development and architecture of a SaaS platform, design and implement AI agent integrations, mentor engineers, own system and DB design (including PostGIS), ensure CI/CD, observability, security, and scalable cloud deployments.

Lead zero-to-one AI product development for commerce: research opportunities, design system-level architectures, prototype with AI coding agents, run live merchant pilots and A/B experiments, analyze results with SQL/notebooks, and ship production features in tight partnership with engineering and founders.