Top Artificial Intelligence Jobs

Lead a new AI-focused data science team accelerating insurance pricing program rollouts. Partner with pricing, delivery, and technology teams to identify AI opportunities; build evaluations for AI and agentic systems; define metrics, statistical tests, ground truth, and production experiments; measure value and risk; translate business needs into requirements; and prototype evaluation tools and AI applications. The role requires insurance pricing expertise, advanced statistics, strong Python and data science skills, modern LLM experience, and effective stakeholder communication.

Build SDKs, frameworks, reference applications, and evaluation tools for Physical AI systems operating across cloud, edge, and real-world hardware. Integrate AI models, inference runtimes, agent frameworks, and connected devices. Maintain development hardware fleets, improve platform security and reliability, document open-source tools, and collaborate with engineering, product, architecture, and industry partners.

Build and deploy generative AI, agentic AI, and large language model solutions in Python. Develop semantic models, ontologies, AI pipelines, evaluation frameworks, and MLOps capabilities. Establish responsible AI governance, model monitoring, and semantic integration standards across enterprise systems. Partner with business and technical stakeholders to deliver measurable outcomes, mentor engineers and data scientists, and influence technical direction across corporate technology.

Lead customer discovery and design end-to-end agentic AI solutions for Altana’s platform. Translate ambiguous business problems into workflows, prompts, evaluation criteria, requirements, and acceptance criteria for deployment engineers. Prototype solutions, guide implementation through launch, measure adoption and business outcomes, train users, and identify expansion opportunities. Partner with sales, customer success, engineering, machine learning, and product teams while developing reusable solution patterns and integration strategies.

Research, design, train, and deploy multimodal AI models using clinical and molecular data to support precision medicine, biomarker discovery, therapy outcome prediction, and companion diagnostics. The role requires collaboration with life sciences companies, regulatory alignment for healthcare products, and publication of original research. Responsibilities include working with pathology slides, genomic profiles, spatial omics, and longitudinal patient records while contributing hands-on model development and product-level engineering.

Designs, prototypes, deploys, and maintains AI copilots, workflow agents, and low-code applications. The role includes stakeholder discovery, workflow mapping, cloud-based development, API and data integration, production deployment, monitoring, prompt and retrieval optimization, and lifecycle ownership. The engineer will also teach AI best practices through workshops, documentation, labs, and office hours while measuring adoption and iterating on solutions.

Leads the internal Applied AI product roadmap, translating operational challenges into AI applications, RAG implementations, agentic workflows, and automation systems. Partners with Finance, Operations, and go-to-market leaders to identify opportunities, define requirements and success metrics, collaborate with engineers and vendors, prototype solutions, drive adoption, and measure business impact. The role also oversees agile development, change management, reliability, and continuous improvement of internal AI tools.

Lead the internal Applied AI product roadmap and transform business workflows across Finance, Operations, and Go-To-Market. Partner with AI engineers and vendors to build applications, prompt pipelines, RAG implementations, and agentic workflows. Define PRDs, success metrics, and roadmaps; oversee agile delivery, prototyping, adoption, change management, reliability, and business impact. The role requires hands-on AI fluency, stakeholder leadership, and experience shipping AI-powered applications or process automation.

Build and deploy foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for latency, cost, throughput, scalability, and hardware utilization. Own complex architectures, reliability SLOs, security and compliance reviews, technical roadmaps, and cross-functional delivery while mentoring engineering teams.

Leads strategy, product vision, roadmaps, and execution for custom AI products developed with Life Sciences partners. Manages the full lifecycle from ideation and proof of concept through development, launch, commercialization, and post-market evaluation. Partners with medical, AI, translational research, engineering, quality, regulatory, and commercial teams while translating customer needs into product requirements. Requires healthcare technology product strategy experience, AI product lifecycle expertise, partner management, and strong knowledge of drug development.

Build and productionize agentic AI harnesses and compound AI systems. Improve agent behavior, reliability, quality, latency, and safety through evaluation-driven engineering. Develop benchmarks, context engineering, orchestration, memory, multimodal I/O, grounding, and optimization solutions. Conduct research, run experiments, deliver production-grade tested code, and collaborate with web and chat platform teams to create effective customer-facing AI experiences.

Lead architecture and implementation of ServiceNow’s AI Control Tower, an enterprise platform for governing AI models, agents, and datasets. Drive products from 1.0 through customer adoption, establish scalable cloud-native and distributed systems foundations, guide AI-native engineering practices, mentor engineers, conduct architecture reviews, and coordinate with product, design, and partner engineering teams. The role requires hands-on technical leadership across SaaS architecture, data platforms, observability, reliability, security, and enterprise AI infrastructure.

Lead AI and data science efforts that accelerate insurance pricing program rollouts. Partner with pricing, delivery, and technology teams to identify AI opportunities, build evaluations for AI and agentic systems, define metrics and statistical tests, measure value and risk, and develop evaluation tooling and AI applications. Apply insurance pricing expertise, modern LLM experience, experimental design, and strong stakeholder communication to move high-value solutions into production.

Supports AI for Operations initiatives across Finance and Business Management by developing business cases, executive insights, OKRs, forecasts, budgets, cost models, and leadership materials. Coordinates cross-functional planning, tracks milestones, manages allocations, supports governance, and drives decision-making across finance, technology, data, and operations teams.

Design, build, deploy, and manage AI systems and automations that improve customer onboarding, support, engagement, retention, and expansion. Develop predictive customer insights, digital experiences, workflows, and integrations with Salesforce, support systems, and data infrastructure. Translate ambiguous Customer Experience requirements into scalable technical solutions, independently make architecture decisions, monitor production systems, measure adoption and ROI, and continuously optimize based on user feedback and changing business needs.

As a Product Architect, you will analyze DRAM system architectures, identify bottlenecks, lead innovations, define memory features, and mentor junior engineers.

Develops and scales generative AI and automation solutions for memory circuit design, layout, verification, simulation, and tapeout workflows. Uses engineering and silicon data for predictive insights and optimization of floorplanning, placement, routing, and validation. Collaborates with design, CAD, product, manufacturing, and process teams to reduce manual effort, improve quality, and accelerate time-to-tapeout. Requires production-quality Python development and experience applying machine learning, data analysis, or automation to engineering workflows.

Design, build, and scale reliable agentic AI systems and infrastructure (orchestration, sandboxed execution, latency optimization, memory, multimodal I/O). Productionize frontier AI algorithms, collaborate with ML/security/product teams, mentor engineers, and deliver production-grade, performant code that advances enterprise AI assistant capabilities.

Leads the Salesforce ecosystem, web applications, and Oracle ERP integrations across global sales, service, and manufacturing operations. Owns business systems strategy, roadmap, governance, support service levels, projects, vendors, security, compliance, data quality, automation, and AI adoption. Partners with business stakeholders, oversees system development and deployment, manages application support escalations, and leads business systems analysts, administrators, developers, and external service providers.

Build and productionize reliable, scalable agentic AI systems (orchestration, sandboxed execution, latency optimization, agent memory, self-reflection, knowledge graphs, multimodal I/O). Implement frontier AI algorithms, ship production-grade code, mentor engineers, collaborate with ML, security, product, and UX teams, and drive roadmap and reliability improvements.

Design, develop, and deploy scalable Voice AI platforms and distributed systems across regions and datacenters. Build audio acquisition, conversation management, speech generation, model integrations, and voice harness capabilities. Partner with product managers and customers, improve SRE tooling, enforce software engineering best practices, and mentor engineering colleagues. The role requires extensive experience with LLM applications, AI agents, programming, Kubernetes, and highly available workloads.

Leads the AI strategy and architecture for supply-chain planning operations. Builds production machine-learning, data-stitching, anomaly-detection, scenario-modeling, alerting, and agentic workflow capabilities across enterprise planning systems. Establishes MLOps, governance, observability, security, and explainability practices while partnering with planners, engineering, manufacturing, procurement, finance, and executives to deliver trusted operational decision support.

Leads portfolio planning and cross-functional execution for Data Science Core and GenAI Studio programs. Establishes roadmaps, operating rhythms, priorities, dependencies, risk management, readiness criteria, decision processes, and success measures. Coordinates data science, engineering, product, research, privacy, legal, security, and business stakeholders from problem framing through deployment, adoption, and monitoring. Communicates technical tradeoffs and responsible AI considerations while improving team effectiveness, capacity planning, documentation, and delivery practices.

Summer 2027 PhD research internship at Atlassian focused on AI research. The role supports distributed-first collaboration, with virtual interviews and flexibility to work remotely or from a company office.

Conduct AI research as a 2027 graduate hire at Atlassian, collaborating remotely or from company offices. The provided description contains no specific research area, responsibilities, qualifications, or technical requirements.