Top AI & Machine Learning Jobs

Conduct an AI-focused research project and contribute tools, pipelines, and systems for Atlassian’s Research and Insights organization. The intern will help develop AI-enabled research workflows, collaborate with a research manager, and support faster insight generation for cross-functional teams. The 12-week, full-time internship is based at Atlassian’s Seattle hub and includes mentorship, technical training, and professional development.

Develop and deploy AI/ML models, data pipelines, and secure production systems. Build scalable architecture for training and inference, transform large datasets, implement NLP and generative AI solutions, and productionize models with data scientists. The role requires Python, Java, ML frameworks, cloud platforms, CI/CD, MLOps, containerization, and responsible AI practices, while contributing to technical communities through demonstrations, presentations, and knowledge sharing.

Lead hands-on development of data infrastructure and retrieval systems supporting agentic AI applications. Build pipelines, APIs, indexing layers, orchestration, memory, and state-management capabilities across enterprise data. Write production Python and PySpark code using Databricks and AWS, enforce data quality, security, compliance, testing, and observability standards, and mentor engineers. Collaborate with product, data science, and business teams while evaluating agentic AI frameworks and responsible AI practices.

Leads data science and analytics initiatives for the credit card business. Translates business questions into analytical solutions, develops statistical and machine learning models, explores large datasets, applies Generative AI, and communicates insights to technical and business stakeholders. Partners across the organization to establish analytical strategies, standards, and scalable products while delivering measurable customer and business impact.

Leads Fusion’s Technical Architecture practice while personally building production AI and LLM tooling for Professional Services. Responsibilities include managing and mentoring architects, setting architecture standards, supporting enterprise presales and regulated customers, modernizing delivery infrastructure, establishing AI security guardrails, evaluating emerging AI capabilities, and driving adoption through training and enablement. The role requires deep Salesforce or ServiceNow expertise, hands-on software development, enterprise integration knowledge, and executive-level customer engagement.

Lead the architecture and development of large-scale machine learning systems, distributed training infrastructure, ranking systems, and production ML services. Drive complex initiatives from concept through deployment, establish engineering standards, partner with product and engineering leadership, and mentor data scientists and engineers. Build reliable, low-latency, high-availability AI platforms using advanced ML frameworks, accelerators, and big data technologies.

Leads Unanet’s internal data strategy and architecture for enterprise AI. Defines the shared data foundation, target architecture, governance standards, roadmap, and reusable approaches for AI assistants, agents, and workflows. Evaluates platforms and integration options, validates technical solutions, and coordinates IT, Security, Legal, Engineering, business owners, and system specialists. Advises senior leaders, establishes data quality and stewardship requirements, and helps determine when data is ready to support scalable AI initiatives.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.

Build and deploy production machine learning systems that optimize Samsara’s global hardware supply chain. Responsibilities include demand, inventory, lead-time, spend, and supplier-risk forecasting; anomaly detection; feature engineering and ETL; real-time inference; MLOps; data infrastructure improvements; governance; and model monitoring. The role defines the AI transformation roadmap, partners with executive and cross-functional stakeholders, mentors scientists, and drives adoption of AI-enabled operational workflows.

Build and deploy production-grade AI and machine learning solutions within regulated customer environments. Develop specialized AI workflows, model deployment and monitoring systems, cloud and data integrations, applications, CI/CD extensions, and secure platform solutions. Advise data science teams, lead customer engagements, troubleshoot infrastructure, create reusable delivery materials, and provide product feedback to engineering, support, and product teams.

Conduct applied AI research and develop foundation models from design through training, evaluation, validation, and production deployment. Collaborate with data scientists, software engineers, machine learning engineers, and product managers to deliver scalable AI products. Lead research initiatives, evaluate emerging methods, optimize large-scale deep learning systems, and translate scientific findings into customer and business impact.

Design, develop, deploy, and optimize large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, orchestration pipelines, and observability. Lead cost-performance governance, technical design reviews, AI standards, roadmap development, and mentorship while improving model scalability, latency, throughput, hardware utilization, and inference costs.

Design, develop, deploy, and support scalable generative AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization across distributed cloud platforms. Own AI system architecture and reliability objectives, lead technical reviews, ensure security and compliance, contribute to long-term platform roadmaps, and mentor engineering staff.

Leads the design, implementation, deployment, and monitoring of scalable AI/ML systems, including generative and agentic AI solutions. Partners with cross-functional teams to translate requirements into production systems, establishes architecture and technical strategy, ensures CI/CD and quality standards, and mentors engineers. The role owns AI/ML initiatives from data exploration and problem definition through deployment and ongoing monitoring, with a focus on responsible AI and healthcare applications.

Leads Business Analysts and Product Managers responsible for Optum Insight’s data mart and financial technology products. Defines product vision, roadmaps, requirements, backlogs, and delivery priorities; oversees testing, data tie-outs, automated SOX controls, and UAT readiness. Partners with technology teams and executive stakeholders to advance financial systems transformation, modern integrations, and data-driven decision-making.

Leads discovery, technical scoping, architecture, development, deployment, and optimization of production AI/ML software. Mentors engineering pods, evaluates emerging AI technologies, collaborates with data scientists and product teams, and ensures model and pipeline reliability, scalability, and performance. The role also promotes responsible AI, engineering best practices, automation, and technical standards.