Top AI & Machine Learning Jobs

Own scalable backend services and full-stack features for Apartment List’s property-listing platform. Design Kotlin and Ruby services, build TypeScript and React interfaces, make architectural decisions, improve reliability and performance, and collaborate with product, design, and analytics. The role includes mentoring engineers, conducting code reviews, shaping technical roadmaps, and solving ambiguous technical problems involving APIs, databases, distributed systems, and marketplace experiences.

Operate and improve enterprise AI/ML platforms across Palantir Foundry, AWS Bedrock, and Amazon SageMaker. Responsibilities include deployment, monitoring, observability, automation, governance, model lifecycle management, incident response, root cause analysis, CI/CD, infrastructure as code, and production reliability. The role provides technical leadership and mentorship while partnering with data, infrastructure, security, architecture, product, and engineering teams to support cloud-based AI workloads and Generative AI adoption.

Works with enterprise clients to drive AI adoption by assessing readiness, identifying use cases, building adoption roadmaps, delivering training, and managing communications and champion programs. Develops prompt guides, AI-enabled workflows, dashboards, and adoption metrics. Facilitates workshops and office hours, supports responsible AI and safety practices, maintains risk registers, and creates reusable resources to scale and sustain AI usage.

Conduct AI and machine learning research through experiments, model evaluation, prototyping, quantitative analysis, and technical documentation. Investigate retrieval and knowledge systems, emerging models, and AI-enabled products while collaborating with researchers and engineers to translate findings into real systems.

Develops and supports computational solutions for faculty research using HPC, AI, cloud, and GPU technologies. Responsibilities include optimizing scientific software and workflows, advising researchers, supporting machine learning tools and model serving, troubleshooting HPC applications, contributing to grant proposals, evaluating emerging technologies, creating documentation and training, and improving research computing services. The role requires close collaboration with multidisciplinary research teams in a hybrid environment.

Build and operate governed, production-grade AI and LLM systems across Velsera’s products and business functions. The role covers multi-cloud model serving, MLOps and LLMOps, evaluation, governance, security, auditability, RAG, agentic workflows, and enterprise integrations. This senior individual contributor will establish reusable AI platform patterns, deliver customer-facing and internal capabilities, support regulated environments, and guide build-versus-buy decisions while collaborating with engineering, product, security, compliance, scientific, and commercial teams.

Build AI-powered digital twins that simulate customer behavior and decision-making. Lead data science and AI initiatives involving LLMs, RAG, agentic systems, experimentation, validation, and performance measurement. Apply behavioral science, UX research, and marketing analytics to develop human-centered models. Partner cross-functionally to translate business problems into scalable solutions, communicate recommendations, and mentor junior team members.

Design, develop, and deploy enterprise AI applications using LLMs, RAG, AI agents, vector search, document processing, and cloud-native technologies. Build scalable APIs, integrations, knowledge platforms, and Kubernetes-based services while contributing to architecture, CI/CD, security, testing, and observability. Collaborate with business and technical stakeholders to solve complex problems, evaluate emerging AI technologies, and mentor team members.

Research enterprise AI use cases through interviews with partners, solution architects, and field sales teams. Analyze qualitative and quantitative findings, identify AI adoption opportunities, map customer needs to Red Hat’s AI platform and partner ecosystem, and recommend partner engagement strategies. Present a final report with strategic recommendations to AI Ecosystem leadership.

As a Machine Learning Systems Engineer, you will optimize distributed training systems, enhance performance profiling, and develop high-performance GPUs for machine learning workloads.