Top AI & Machine Learning Jobs

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.

As a Machine Learning Engineer in Fraud Risk, you'll build and maintain ML systems for fraud detection and anomaly analysis, ensuring robust performance and real-time decision making.

Lead architecture and technical vision for integrations that ingest external data (e.g., Salesforce, HubSpot) into Jasper's IQ layer. Design scalable, secure data ingestion, define context primitives, drive cross-team alignment, and deliver staff-level initiatives that improve reliability and AI-driven product quality.

Lead AI-driven strategic projects across growth and case automation: translate workflows into executable plans, prioritize opportunities with product and engineering, build measurement frameworks, align cross-functional teams, and interface with senior stakeholders.

Leads Odyssey Logistics’ enterprise AI program, setting strategy across predictive, agentic, generative, and simulation applications. The role manages AI leads, prioritizes use cases, scales solutions into production, establishes governance, and measures business value. It requires senior leadership, hands-on AI architecture experience, cross-functional influence, and experience building AI capabilities or governance in complex, asset-heavy organizations.

Build and operate AI agent applications that analyze enterprise code, identify and prioritize security risks, propose and verify fixes, and open merge-ready pull requests. Develop context engineering, evaluation benchmarks, and reliable production systems across thousands of repositories. Partner with customers on integrations, review code, and mentor engineers. Requires deep Python expertise, agentic workflow experience, application security knowledge, and production software development experience.

Architect and build terabyte-scale robotics data pipelines from fleet sensor streams through training ingestion. Curate, preprocess, annotate, version, and validate datasets; analyze data coverage, drift, redundancy, and quality; mine scenarios using embeddings and models; and develop samplers, benchmarks, and evaluation datasets. Partner with ML researchers to diagnose model failures and create targeted data mixtures and processing workflows that improve robot-learning performance.

Conduct independent robot-learning research from hypothesis through data, model training, deployment, and evaluation. Establish long-term research direction, experiment with novel architectures and post-training methods, develop data strategies, deploy models on physical robots, define real-world metrics, and build scalable machine-learning infrastructure for research and production.

Build and optimize end-to-end machine learning training infrastructure for large-scale multimodal robot data. Responsibilities include distributed training, data ingestion and transformation, checkpointing, orchestration, experiment tracking, sampling and curation, performance profiling, debugging data and training issues, and converting research prototypes into production infrastructure. The role may also involve robot learning, cloud and cluster infrastructure, post-training, on-robot inference optimization, CUDA or Triton kernels, and video-heavy datasets.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and adoption enablement. The role also involves collaborating on React and TypeScript user experiences, translating ambiguous requirements into scalable systems, and communicating technical tradeoffs across product, engineering, and dairy science teams.

Cinder is building a paid expert network for specialists who test AI models for safety risks. Projects may involve red teaming, jailbreak testing, image, video, audio, agentic, and MCP-connected systems. They seek experts in areas such as child safety, CBRNE, extremism, cybersecurity, fraud, self-harm, psychology, copyright, and multilingual or culturally specific model evaluation.

Build reinforcement learning systems from the ground up, including environments, reward functions, training pipelines, evaluation frameworks, and production systems. Train and fine-tune models using RL methods, scale training across GPU clusters, conduct experiments, and guide technical direction. The role also contributes to engineering culture and future hiring while working closely with company founders in an on-site early-stage environment.