Top AI & Machine Learning Jobs

Lead client engagements using data science, AI, advanced analytics, statistical modeling, experimentation, and marketing measurement. Manage and develop analysts and data scientists, advise quantitative researchers, pitch projects, grow client relationships, and provide thought leadership on machine learning and emerging AI methods. Translate technical work into strategic recommendations for clients and executives.

Build and improve an autonomous AI coding agent that understands tasks, generates and tests code, self-repairs, reviews changes, and integrates with developer tools. Design codebase retrieval and knowledge systems, build scalable Python services and APIs, leverage LLMs, develop agent evaluations, and identify automation opportunities with platform and product teams. The role requires strong software engineering, cloud infrastructure, CI/CD, testing, and end-to-end ownership.

Lead enterprise AI and automation deployments from validated prototypes through secure, supported production. Manage the AI deployment lifecycle, technical standards, architecture patterns, vendor and platform strategy, governance, security, monitoring, and operational resilience. Partner with Information Technology and operating brands to integrate solutions, establish production ownership, oversee support, and evaluate emerging AI technologies. Lead technical employees, manage budgets, develop talent, and represent AI in enterprise governance forums.

Develop, optimize, and deploy production machine learning models for autonomous vehicle perception, prediction, planning, and scene understanding. Own the full ML lifecycle, build scalable training and data pipelines, optimize neural networks with quantization and TensorRT, and integrate models into C++ vehicle systems. Analyze simulation and real-world driving data, improve reliability and latency, and collaborate with autonomy, hardware, systems, and infrastructure teams.

Partners with AI labs to integrate models, run evaluations, deliver results, and manage customer relationships. Builds pragmatic full-stack solutions, automations, onboarding workflows, and delivery tooling for cutting-edge AI teams. Translates customer and research feedback into product and engineering initiatives, supports strategic account growth, and serves as a technical point of contact across onboarding, releases, and issue resolution.

Analyze blockchain and blockspace datasets to identify trends, build data pipelines, perform statistical analysis, develop models, and create dashboards. Collaborate with cross-functional stakeholders to deliver data-driven solutions, improve metrics and infrastructure, conduct research, and explore opportunities in blockchain, AI, and data science.

Owns product measurement and decision thresholds across Ascend’s financial operations platform. Responsibilities include defining accuracy, success, and exception metrics; auditing production scores; partnering with Product and Engineering on instrumentation; deploying models for confidence scoring, reconciliation, collections, and recovery; monitoring calibration, drift, and realized outcomes; designing experiments; and using product data to inform the roadmap.

Designs, builds, and deploys enterprise AI solutions for pharmaceutical and healthcare marketing operations. Leads client discovery, translates business needs into scalable architectures, develops integrations and AI workflows, and delivers production-ready cloud solutions. Responsibilities include generative AI, RAG, data pipelines, enterprise systems integration, authentication, observability, CI/CD, Docker, Kubernetes, and Google Cloud Platform. Serves as a technical advisor while collaborating with product, engineering, data, security, and delivery teams.

Build and operate production systems for Juniper Square’s AI Platform, including APIs, infrastructure, reusable frameworks, and AI/LLM capabilities. Own projects end to end, ensure reliability and observability, contribute to technical design, and support incident response. Collaborate with product, design, QA, and engineering teams while applying AI-native development practices. The role requires strong Python backend engineering, cloud-native technologies, databases, production operations, and hands-on experience with RAG, agents, evaluation workflows, or related AI systems.

Develop and deploy computer vision, reinforcement learning, and multimodal machine learning systems for biomedical imaging and pathology research. Train and benchmark models, improve computational workflows, and support closed-loop microscopy experiments. Collaborate with biologists, imaging scientists, and engineers, contribute to manuscripts and grant proposals, and communicate technical concepts to nontechnical stakeholders. The role focuses on applying AI to neurodegeneration and other disease-related research.