Top AI & Machine Learning Jobs

Design, train, evaluate, deploy, and own production machine learning models for transaction categorization, risk scoring, and financial data enrichment. Build scalable ML systems and automated data pipelines across large, diverse datasets. Partner with product, data science, and engineering teams, improve model reliability and accuracy, explore emerging AI techniques, and mentor engineers.

Lead technical direction and hands-on development of ML and AI Context systems for HubSpot CRM: model development, productionization, retrieval, embeddings, evaluation, monitoring, and cross-team architectural leadership to deliver measurable product and business impact.

Lead development and execution of MVB's AI and automation strategy, including Generative and Agentic AI and RPA. Partner with business and technical stakeholders to design, pilot, and scale solutions, establish governance and best practices, monitor performance metrics, manage vendors, and deliver training to ensure ethical, secure, and effective AI deployments.

Design and implement scalable, distributed, AI-driven SD-WAN and AIOps services. Develop real-time, fault-tolerant message/event-driven systems, write and test code, participate in design/code reviews, and drive end-to-end feature delivery and operability improvements.

Design and implement large-scale, distributed AI-driven SD-WAN systems and real-time, fault-tolerant services. Drive features end-to-end from design through testing and deployment, improve scalability and operability, participate in code and design reviews, and collaborate with product and documentation teams.

Design and scale AI-powered sales enablement experiences: architecture for generative AI, RAG, AI agents, content discovery, guided selling, coaching automation, governance, pilots, integrations with enablement/CRM/LMS systems, and measure adoption and business impact.

Develop and deploy scalable statistical and machine learning models for ecommerce fulfillment and delivery optimization. Responsibilities include exploratory data analysis, feature selection, hypothesis testing, data management, governance, data quality, workflow automation, visualization, and communicating insights to cross-functional teams. The role translates business requirements into analytical solutions, validates production models for performance and scalability, and mentors junior associates.

Leads the design, development, and production deployment of personalization and recommendation machine learning systems. Builds deep learning, ranking, user modeling, generative AI, and LLM solutions at petabyte scale. Defines experimentation and evaluation frameworks, drives AI architecture and roadmap decisions, operationalizes real-time ML systems, monitors model performance, and mentors senior data scientists while leading cross-functional technical initiatives.

Build scalable AI and ML platforms for autonomous retail space planning, including feature stores, model runtimes, agentic workflows, and CI/CD infrastructure. Own the full software lifecycle from architecture and coding through deployment, monitoring, and maintenance. Lead system design reviews, influence technical decisions, mentor engineers, and align cross-functional stakeholders on rollout strategies. Apply modern programming languages and cloud-native practices to deliver secure, modular, and resilient enterprise systems.

The role involves architecting and scaling Generative AI capabilities, automating workflows, and ensuring secure AI integration in products, with a focus on safety and efficiency.

Build and iterate on backend APIs and data pipelines for AI/ML proof-of-concept projects. Integrate LLMs, embedding, vision, and speech models; evaluate providers; and summarize technical findings. Collaborate with stakeholders to clarify requirements, contribute to product discussions, document decisions, and manage multiple workstreams. The role requires REST API development, SQL and non-relational data familiarity, adaptability in ambiguous environments, and strong communication.

Provide SETA support to government teams building and evaluating AI-enabled applications. Advise on AI design, prototypes, integration, and technical risk. Assess LLMs, RAG, agentic workflows, orchestration frameworks, and evaluation harnesses. Review architectures, recommend engineering tradeoffs, develop prototypes and guidance, and support test and evaluation. Collaborate with platform engineers, product leads, and mission users to ensure operational relevance and maintain current knowledge of emerging AI tools and practices.

Designs and integrates production AI/ML capabilities for USCYBERCOM, including LLM agents, orchestration workflows, RAG pipelines, vector databases, model routing, MCP tool integrations, evaluation pipelines, and governance guardrails. Provides embedded operational support, rapid prototyping, capability integration, and model benchmarking. Partners with data scientists and mission analysts to operationalize research, evaluate emerging AI technologies, and deliver secure, enterprise-scale mission capabilities.

Client-facing Associate leads workstreams for process improvement, intelligent automation, and AI-enabled transformation. Responsibilities include process discovery, facilitating workshops, analyzing processes and data, designing future-state processes and implementation roadmaps, preparing executive deliverables, partnering with technical teams to design and deploy automation, and supporting change management. Mentors Analysts and contributes to proposals, methodologies, and thought leadership. Requires strong collaboration and consulting skills, frequent travel, and 2+ years relevant experience.

As a Staff Machine Learning Engineer, you will lead the development and deployment of AI models for autonomous driving, focusing on safety and reliability. Responsibilities include driving the roadmap, training models, and collaborating across teams.