Top AI & Machine Learning Jobs

Develop AI/ML features using LLMs, generative AI, RAG, conversational agents, and machine learning models. Build production pipelines, integrate model APIs, evaluate quality and performance, and create secure full-stack services. Collaborate with product, architecture, infrastructure, security, and QA teams while documenting solutions and contributing to code reviews.

Designs, builds, tests, and supports NICE Cognigy virtual agents for digital chat and omnichannel self-service. Develops conversation flows, intents, entities, NLU models, integrations, and live-agent handoffs. Monitors bot analytics and interaction data, troubleshoots production issues, maintains documentation, and ensures security, privacy, accessibility, and compliance. Collaborates with business, IT, product, and experience design teams on discovery, releases, and continuous automation improvements.

Build and deploy LLM-powered features for clinical, patient, and operational workflows. Responsibilities include prompt design, retrieval, tool use, agent orchestration, evaluation harnesses, feedback loops, production operations, and AI engineering tooling. Partner with clinical, product, and safety stakeholders to ensure responsible healthcare-grade AI. Mentor teammates, communicate model behavior clearly, and stay current with emerging AI research and development practices.

Build and own Clera’s AI/ML intelligence layer, including recommendation systems, ranking algorithms, and LLM-powered candidate-role matching. Develop production-ready features, data pipelines, and services while collaborating cross-functionally and shipping frequently. The role requires hands-on experience with the listed development tools, TypeScript, React, startup environments, and a willingness to work six days per week in a San Francisco hacker house.

Build and deploy AI-powered tools, developer workflows, testing pipelines, integrations, and internal interfaces across the organization. Evaluate frontier models and emerging AI tools, improve CI/CD and automated testing, support go-to-market and operations teams, and establish practices for agentic engineering, security, responsible AI, reliability, and cost management. The role requires cross-functional collaboration and rapid prototyping.

Own the technical design and delivery of multi-component AI systems, including LLM-backed features, agent tool orchestration, evaluation frameworks, data pipelines, and model harnesses. Build reliable APIs, SDKs, and feedback loops; optimize latency and cost; assess external models and datasets; and support production systems through on-call rotation. Mentor engineers and collaborate across search, location intelligence, and platform teams.

Develop and deploy AI and machine learning models for production optimization, quality control, anomaly detection, predictive maintenance, supply chain forecasting, and operational analytics. Build scalable data pipelines, MLOps processes, APIs, dashboards, and LLM-powered solutions using prompt engineering, RAG, fine-tuning, and AI agents. Analyze complex datasets, conduct statistical experiments, monitor model performance, and promote responsible AI practices.

Direct autonomous coding agents on production work, review agent-generated pull requests, author evaluation suites, and own software quality across correctness, performance, security, and WCAG accessibility. The engineer will operate within a FedRAMP-authorized environment, maintain secure agent boundaries, advance autonomy based on reliability data, improve development lifecycle patterns, and mentor engineers adopting agent-directed delivery.

Analyzes financial and ERP data using statistical analysis, data mining, predictive modeling, and advanced algorithms. Supports Oracle financial shared services, data conversion, business continuity, disaster recovery, and contingency planning. Develops analytical outputs that improve agency KPIs and identifies trends and business opportunities. Requires collaboration with systems, network, hosting, and disaster recovery teams, plus participation in COOP exercises and Oracle environment upgrades.

Designs, builds, and maintains AI-assisted automations across accounting and operational systems. Responsibilities include integrating systems through APIs and data pipelines, developing LLM and OCR workflows, improving data quality, monitoring automation reliability, documenting processes, supporting data governance, and automating acquisition-related normalization and migration tasks. The role partners with Accounting, Land, Operations, IT, and Digital teams to identify automation opportunities and deliver scalable solutions.