Top AI & Machine Learning Jobs

Citizen Health seeks AI/Machine Learning Engineers to develop AI solutions for healthcare using advanced ML techniques, focusing on rare diseases and patient care. Responsibilities include designing ML models, optimizing LLMs, and ensuring compliance.

Report to the CEO to identify high-leverage workflows across non-engineering teams, diagnose constraints, and build AI-native solutions (agents, automations) that drive commercial outcomes and reusable patterns. Set AI-native operating standards, decide build vs buy, and scale AI leverage across Sales, CS, Marketing, Ops, and Talent.

Lead design, deployment, and management of AIOps solutions using Splunk and MLTK to provide predictive analytics, closed-loop incident remediation, and secure telemetry integration with ServiceNow and SolarWinds. Architect observability across classified/unclassified enclaves, deploy ML models, automate remediation, and ensure compliance with DoW STIGs and IL5/IL6.

The Senior Solutions Engineer will design and implement infrastructure for AI and HPC workloads, engage with customers, and lead technical discovery and architecture design.

Develop and scale state-of-the-art computer vision pipelines for aerial and oblique imagery. Train and improve segmentation, detection, and generative models, run large-scale PyTorch experiments, maintain modular ML infrastructure, and collaborate on deployment and inference optimizations while reproducing top-tier research.

Design, build, and maintain end-to-end agentic intelligence systems that ingest social, on-chain, and ads data; create pipelines, semantic layers, and multi-step AI agents; ship full-stack apps and dashboards; collaborate with marketing and client teams to operationalize structured intelligence and measurable impact.

Lead and hands-on build production-grade agentic AI/ML systems and automation pipelines. Manage engineers, own technical roadmap, design orchestration, implement RAG/vector retrieval, develop predictive models, maintain data engineering and MLOps infrastructure, and engage stakeholders to deliver reliable end-to-end AI solutions.

The role involves designing and maintaining large-scale ML infrastructure, optimizing distributed training systems, and enhancing computing performance for model training.

Lead and execute StarCompliance's data and AI strategy: build a governed data foundation, deliver production-grade LLM and ML capabilities (RAG, semantic search, vector DBs), establish MLOps, and grow a cross-functional Data & AI team aligned with product and business priorities in a regulated financial environment.

Lead development of an agentic AI platform: build agent orchestration, RAG/CAG pipelines, embeddings and vector-store ops, integrate LLMs (Anthropic Claude primary), deliver full-stack features across Python/Node/React, own production deployments, evaluation pipelines, and mentor a small senior engineering team while working hybrid in Denver.