Top AI & Machine Learning Jobs

Develops production-grade statistical and machine learning models and predictive solutions for healthcare operations and business performance. The role analyzes complex data, designs experiments, evaluates AI systems, establishes metrics, creates dashboards, standardizes reporting, and communicates insights to technical and executive stakeholders. It also supports generative AI evaluation, model deployment workflows, and data-driven decision-making in regulated industries.

Build and own a scalable API platform that enables external organizations to upload data, fine-tune and evaluate AI models, and run low-latency inference for robotics applications. Responsibilities include designing multi-tenant infrastructure, APIs, deployment integrations, observability, reliability, and developer tooling. The role requires strong backend and systems engineering skills, Python expertise, production machine learning familiarity, and end-to-end ownership of ambiguous platform problems.

Develop and optimize CUDA kernels and machine learning software for training and inference on advanced GPUs. Profile workloads, improve GPU utilization, build benchmarking and automation tools, troubleshoot system bottlenecks, support new model architectures, validate numerical and gradient correctness, review code, and contribute to technical documentation and industry events.

Build and optimize production AI pipelines for healthcare workflows using LLMs, classical ML, VLMs, OCR, retrieval, agents, and fine-tuned models. Develop document extraction systems, evaluation harnesses, monitoring, and inference orchestration. Improve model quality, latency, cost, explainability, and safety while collaborating with product, engineering, senior ML engineers, and healthcare domain experts.

Lead the architecture and hands-on development of AI agents and autonomous storefront systems. Build agent tools, execution loops, evaluations, observability, MCP infrastructure, quality gates, and optimization workflows. Design APIs, data models, and service boundaries across TypeScript, Node.js, React, Postgres, MongoDB, and Redis. Drive AI-native engineering practices, testing, reliability, technical communication, and cross-functional technical leadership.

Supports enterprise data modeling, integration, quality improvement, and machine-learning projects in a manufacturing environment. Responsibilities include product-family classification using BOM data, feature development, model testing, validation, MLOps and AI research, agentic workflow mapping, controlled AI testing, documentation, and communicating findings to technical and business stakeholders. The intern collaborates across departments while gaining practical experience in data engineering, machine learning, and AI-enabled process improvement.

Develops and deploys data science solutions for information operations and national security customers. Responsibilities include statistical modeling, machine learning, deep learning, simulation, network analysis, multilingual text analytics, data visualization, experimentation, prototyping, customer engagement, and technical translation. The role also supports deployment in cloud, containerized, and air-gapped environments, including configuration, monitoring, troubleshooting, and maintaining customer systems. An active Top Secret clearance with SCI eligibility is required, and the position is onsite in the DC Metro area up to four days weekly.

Designs, develops, and deploys enterprise Generative AI solutions using LLMs, semantic search, and AI orchestration. Builds monitoring systems, improves AI architecture, supports prompt engineering and evaluation, reviews code, and mentors engineers. Collaborates with product leaders and stakeholders to deliver secure, scalable, customer-facing AI applications. Requires Azure OpenAI, Azure AI Search, Semantic Kernel, DevOps, CI/CD, testing, and advanced Python expertise.

Supports the rollout and adoption of AI capabilities across Capital Partners by delivering workshops, onboarding, guidance, playbooks, and learning materials. Coordinates AI pilots, product releases, change plans, and readiness activities; maintains use-case inventories and knowledge assets; gathers user feedback; monitors adoption metrics; and recommends improvements. The role also facilitates process mapping, supports responsible AI usage and governance, manages stakeholder follow-through, and helps move AI initiatives from pilot to production-ready solutions.

Develop and optimize large-scale machine learning models, open-source LLM infrastructure, and training and inference systems for advertising applications. Build distributed data and model pipelines, adapt DeepSpeed and OpenRLHF, optimize GPU/TPU serving for latency and cost, monitor model performance, and collaborate with applied scientists on research. The role focuses on recommender systems, personalization, targeting, reinforcement learning, and ad intelligence at massive scale.