Top AI & Machine Learning Jobs

Define clinical reasoning, safety boundaries, evaluation scenarios, datasets, rubrics, and acceptance thresholds for AI-enabled chronic disease products. Analyze failures and edge cases, support clinical validation and outcomes research, interpret clinical and EHR data, and collaborate with Product, Engineering, AI/ML, Data, informatics teams, and clinical stakeholders on integrations and workflows.

Build backend infrastructure for an AI revenue engine. Integrate large language models, optimize data pipelines, and develop scalable systems supporting autonomous revenue workflows for companies in the physical economy.

Own and develop a production recommendation engine for Kubernetes resource optimization. Design time-series forecasting models, data-quality pipelines, safety guardrails, regression tests, and production metrics for CPU, memory, GPU, and JVM workloads. Investigate customer issues, improve recommendation accuracy, maintain Python services, and guide ML strategy while collaborating with platform engineering, product, and leadership.

Develops and executes the organization’s AI and automation strategy, overseeing projects from prototyping through deployment and maintenance. Leads and mentors AI specialists, data scientists, engineers, and analysts while collaborating with IT, product, operations, and compliance teams. Establishes AI development and governance practices, ensures regulatory and ethical compliance, manages budgets and resources, tracks project risks and outcomes, and reports to senior leadership. Promotes innovation and continuous professional development.

Develop AI-powered automation workflows, LLM tools, prompt-engineering solutions, analytics dashboards, APIs, and test automation frameworks for semiconductor manufacturing quality and testing. Collaborate with cross-functional teams to identify automation opportunities and implement machine-learning solutions that improve workflow efficiency, test coverage, and manufacturing decisions.

Develop deep learning methods for protein language modeling, structural modeling, and multimodal representation learning of TCR-pMHC interactions. Apply machine learning, statistics, computational biology, and data science to complex biological datasets while collaborating with wet-lab scientists. The role includes training and fine-tuning protein and structure-prediction models, building AI pipelines, and using rational structure-based and physics-informed approaches to support therapeutic discovery.

Design, build, and maintain optimization technology, full-stack systems, and data-intensive scheduling infrastructure. Improve optimization algorithms using linear optimization, machine learning, and generative AI. Lead complex technical initiatives, establish testing and observability, support scalable operations, troubleshoot customer issues, participate in on-call rotations, and mentor engineers while partnering closely with product and engineering teams.

Design and implement evaluations for AI agents across voice, vision, and browser use. Build benchmarks, simulations, and scoring systems for customer-specific scenarios, then use evaluation findings to improve agent behavior, context, and underlying models.

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.