Top AI & Machine Learning Jobs

Leads Bedrock’s enterprise data, analytics, and AI strategy, roadmap, governance, platforms, integrations, reporting, and automation. Oversees technical teams, evaluates and implements Microsoft data platforms, translates business needs into solutions, manages AI programs, and establishes data quality, security, compliance, adoption, and performance standards across the organization.

Optimize visual AI model inference across architectures, algorithms, runtimes, GPU kernels, and distributed systems. Profile workloads, identify compute and memory bottlenecks, improve latency, throughput, memory efficiency, and GPU utilization, and develop benchmarking infrastructure. The role involves techniques such as quantization, sparsity, caching, compilation, attention optimization, custom CUDA or Triton kernels, and multi-GPU execution while collaborating closely with research scientists.

Leads solution architecture, technology modernization, and AI initiatives for an automotive marketplace domain. Designs scalable solutions, facilitates architecture reviews, establishes standards, oversees software development compliance, and identifies AI use cases. Develops agent orchestration strategies, leads proofs of concept, mentors engineering teams, evaluates emerging AI technologies, and bridges business and technical stakeholders.

Designs and ships production multi-agent AI systems for KYB, underwriting, case review, and risk monitoring. Owns agent architecture, retrieval, tool integrations, evaluations, observability, MLOps, reliability, and compliance guardrails. Requires strong Python engineering, agent framework expertise, distributed systems experience, Kubernetes operations, and production LLM deployment skills. Mentors engineers and partners with product, security, compliance, and applied science teams. Remote employees travel to Orlando at least twice annually.

Build and operate production AI agents, agentic workflows, MCP servers, and CX applications. Develop Python and TypeScript services, AWS infrastructure, integrations, testing, observability, security controls, and CI/CD pipelines. Partner with Customer Success and other CX teams to prototype solutions, translate workflows into technical plans, and deliver reusable platforms. Mentor engineers and non-engineers, document systems, establish governance, and support reliable handoff and shared ownership.

Build scalable AI and machine learning platform capabilities across model development, deployment, monitoring, cataloging, tagging, and LLM hosting. Collaborate with customers and cross-functional teams to integrate platforms such as AWS SageMaker and Azure ML. Develop secure APIs and distributed backend systems, optimize cloud and containerized services, and establish robust testing and CI/CD pipelines.

Lead research, development, deployment, and measurement of machine learning models for forecasting, pricing, inventory optimization, personalization, and generative AI. Build production-grade software and scalable machine learning workflows, collaborate with engineering and business teams, communicate insights to stakeholders, and support enterprise data science applications across testing and production environments.

Embedded Data Scientist for Superhuman Mail, partnering with product, growth, engineering, and design teams to guide roadmap decisions. Responsibilities include defining product metrics, designing experiments, applying causal inference and machine learning, measuring AI feature quality, analyzing activation and retention, identifying growth and conversion drivers, and communicating insights to stakeholders. The role requires strong Python, SQL, experimentation, statistics, and cross-functional influence skills.

Design, implement, and maintain production-oriented NLP and LLM models; build data pipelines and HPC analyses; integrate ML solutions with clinical research tools; ensure data quality, privacy compliance, and collaborate across multidisciplinary teams.

Own the infrastructure, deployment, monitoring, versioning, and rollback systems that move computer vision and machine learning models from development into reliable cloud and edge production environments. Process and optimize pipelines handling 11 million daily images, support roadside hardware deployments, detect model degradation, and manage updates and retraining. The role requires strong production software engineering skills, adaptability, and interest in operationalizing ML at scale.