Top AI & Machine Learning Jobs

Conduct independent AI research for predictive toxicology and drug safety. Develop novel deep learning architectures, multimodal models, representations, uncertainty methods, and mechanistic reasoning approaches for heterogeneous biological and chemical data. Design rigorous experiments, benchmarks, ablations, and generalization evaluations while collaborating with engineering and scientific teams to translate validated research into scalable modeling systems. Contribute to scientific strategy, publications, collaborations, and regulatory validation.

Leads Nuro’s Software Operations organization, overseeing AI training and evaluation data, autonomy regression analysis, release validation, operational KPIs, and root-cause investigations. Manages large-scale data-labeling vendors, including quality, throughput, SLAs, budgets, and pricing. Partners with engineering, machine learning, data science, simulation, and product teams to build evaluation datasets, improve autonomy performance, and scale operational processes and teams.

Build and deploy production generative AI and machine learning systems for utility clients. Own engagements end to end, including discovery, architecture, retrieval, model selection, evaluation, deployment, monitoring, operator-facing applications, and post-go-live support. Integrate with client data and business systems, establish AI safety and governance controls, manage scope and risk, and communicate with technical teams and executives. Contribute reusable accelerators, evaluation frameworks, and reference architectures across client engagements.

Develop and manage intelligent automation solutions using UiPath, bridging communication between project managers and clients, conducting process analysis, and providing documentation.

Owns statistical, Bayesian, and machine learning solutions from development through production. Builds models for lead scoring, retention, forecasting, and segmentation; applies LLMs to unstructured data; develops Databricks and Azure pipelines; creates React and FastAPI applications; and communicates analytical findings to business stakeholders across sports and entertainment functions.

Supports Dunmore’s AI and data initiatives through data modeling, integration, quality improvement, machine-learning experimentation, product and BOM classification, and agentic workflow analysis. The intern prepares data, develops features, tests and validates models, documents research and results, maps business processes, and evaluates AI workflows with appropriate safeguards. The role involves collaboration with technical and cross-functional teams and communication of findings to technical and non-technical stakeholders in a manufacturing environment.

Lead the architecture, development, optimization, and operation of production LLM and AI automation systems. Design capabilities including agents, retrieval, code generation, tool use, model routing, evaluation, and observability. Drive platform modernization, scalability, reliability, architecture, and code quality while mentoring engineers and influencing technical practices across teams. Build prototypes, establish engineering standards, conduct reviews, and communicate complex technical concepts through documentation and cross-functional collaboration.

Analyze business problems using SQL, Python or R, and statistical methods to identify trends, test hypotheses, and develop insights or recommendations. Visualize findings in Tableau and collaborate with data engineers to present results to stakeholders. The role requires clear communication, flexibility, and the ability to explain analytical approaches and adapt deliverables to business needs.

Early-employee, open-call role for candidates passionate about accelerating UAV swarm and spatial AI capabilities for national security. Help shape company direction, propose a role, and describe relevant past projects. Requires optimistic, tenacious, self-determined, and highly motivated individuals. Must meet ITAR eligibility (US Citizen, Green Card holder, or asylee/refugee).

Own the evaluation, selection, optimization, and production deployment of large language models powering legal and medical document workflows. Build EvalOps and monitoring frameworks, benchmark model quality, cost, speed, and resilience, manage model transitions, optimize RAG and orchestration pipelines, and reduce hallucinations. Partner with product and go-to-market stakeholders while independently implementing changes across staging and production.