Top Senior Machine Learning Jobs in Denver & Boulder, CO

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Designs, builds, and operationalizes enterprise AI, generative AI, machine learning, and agentic AI solutions. Develops integrations, orchestration services, reusable tooling, deployment pipelines, monitoring, and governance controls. Partners with architecture, platform, data, and product teams to deliver scalable, secure, compliant AI capabilities across the organization.

Design, train, deploy, monitor, and optimize speech and NLP models for a voice-agent platform. Build MLOps pipelines, APIs, and inference services while developing Android and iOS applications that integrate AI, voice, real-time communication, and backend services. Create frontend prototypes and dashboards, conduct model experiments, evaluate vendors, optimize performance, document systems, and maintain engineering quality across collaborative teams.

Build and operate production machine learning systems for Airbnb Payments, including LLM agents, real-time fraud detection, anomaly detection, personalization, and checkout optimization. Design, train, deploy, monitor, and continuously improve batch and streaming AI applications at global scale. Partner with product, engineering, operations, and data science teams, establish model governance and testing standards, and mentor engineers.

Own the full machine learning lifecycle for search ranking and retrieval, including model training, deployment, serving, feature pipelines, evaluation frameworks, hybrid lexical-vector retrieval, large-scale embedding inference, query understanding, and permissions-aware multi-tenant search. Collaborate with infrastructure, AI, and backend teams to improve search relevance across ClickUp’s platform.

Build and operate production retrieval-augmented generation systems, including embeddings, vector retrieval, grounded language-model generation, citation validation, prompt and schema design, model packaging, serving, rollback, and telemetry. Develop bounded model assistance for narrative extraction with provenance and rule-context tracking. Maintain resilient serving paths in secure government cloud environments, including restricted or air-gapped deployments.

Own the machine learning and applied AI layer of a software platform, from research through production. Build text-processing pipelines, extract structured data from technical documents, develop LLM-powered decision workflows, create evaluation systems, and build NLP classifiers. Lead exploratory research, translate findings into product features, and collaborate with a small engineering team. The role is remote in the US or Canada, with quarterly travel.

Develops AI/ML algorithms and ETL infrastructure supporting LLM, NLP, predictive analytics, and other customer solutions. Builds and maintains data pipelines, curates evaluation data, improves scalability and automation, and collaborates with researchers and developers using Agile methods. The role requires Python-based data engineering, machine learning experience, and an active TS/SCI clearance, with preferred experience in cloud, containers, distributed computing, reinforcement learning, GenAI operations, and GPU programming.

Design, develop, and deploy production-grade AI/ML solutions for national security missions, including NLP, LLMs, generative AI, deep learning, and computer vision. Build scalable data pipelines, APIs, microservices, MLOps workflows, and cloud or edge-deployed systems. Optimize and evaluate models, integrate them into secure operational environments, and collaborate with engineering and mission teams. Document technical designs and support accreditation processes in classified settings.

Leads the design, development, deployment, and operation of scalable machine learning solutions and high-performance microservices. Builds real-time inference systems using Triton and TensorRT, guides distributed cloud architectures, participates hands-on in coding and reviews, and mentors engineers. Drives technical strategy, operational excellence, system resilience, and business-wide innovation across generative AI, graph machine learning, and big data initiatives.

Develop AI/ML and LLM solutions by building ETL pipelines, curating evaluation data, implementing machine learning algorithms, and improving scalable infrastructure. The role collaborates with researchers and developers, supports NLP and predictive analytics, applies Agile methodologies, and may lead interdisciplinary teams. Work includes data engineering, cloud and containerized deployment, GenAI operations, and algorithm implementation across varied customer missions.

Develop and implement AI/ML algorithms across computer vision, NLP, time series, reinforcement learning, graph data, and large language models. Analyze multimodal datasets, apply research findings, support training and deployment pipelines using AWS, create technical documentation, and mentor team members. The role requires Python and deep learning expertise, version control experience, and the ability to obtain a TS/SCI clearance.

Lead exploration and application of large language models and generative AI for enterprise contact-center automation. Translate research into high-performing, deployable ML systems; contribute across experimentation, implementation, and deployment; collaborate with engineering and product teams; optimize performance; and help shape team strategy, innovation, and professional growth.

Lead exploration and application of large language models and generative AI, translate research into deployable ML systems, and guide strategic direction. Develop solutions across experimentation, implementation, and deployment while collaborating with engineering and product teams to optimize performance and customer outcomes in contact center automation.

Lead the end-to-end ML execution: build and operate data pipelines, training and evaluation systems, and scalable inference. Fine-tune and optimize large models (LoRA/QLoRA/SFT/DPO/distillation), ensure GPU efficiency, monitor production quality, collaborate with product and application engineering, and rapidly ship reliable, safe ML solutions that translate research into production.

Evaluates emerging machine learning technologies and designs scalable, secure enterprise architectures covering data ingestion, feature engineering, training, deployment, monitoring, governance, and responsible AI. Partners with engineering, data science, platform, risk, and security teams to establish MLOps standards, reusable solution patterns, and production deployment strategies. Provides technical leadership, evaluates vendors, guides implementation, and translates complex ML architecture recommendations for technical and non-technical stakeholders.

Design, productionize, and scale machine learning and optimization models, pipelines, libraries, and deployment platforms. Partner with data scientists, engineers, product managers, and business stakeholders to deliver ML capabilities, personalization systems, and reusable end-to-end workflows. Establish ML engineering best practices, troubleshoot data pipelines, mentor junior professionals, and help define BetterHelp’s ML platform strategy.

Design and productionize machine learning, deep learning, LLM, personalization, recommendation, search, and retrieval systems. Collaborate with product, engineering, and analytics teams to deploy AI solutions that improve customer experiences, business growth, and operational efficiency. The role requires driving projects from ideation through deployment and delivering measurable business impact in a fast-moving fintech environment.

Own the full lifecycle of production classification and detection models, including transformer fine-tuning, entity detection, behavioral risk scoring, training, serving, versioning, evaluation, drift detection, and retraining. Build SageMaker-based ML systems and define output contracts supporting audit and policy enforcement. Lead the evaluation harness and mentor a co-op while operating as the company’s sole ML engineer.

Develop and integrate production AI/ML capabilities into Oracle E-Business Suite on OCI for federal financial management. Build NLP and document-processing solutions, REST API integrations, ML lifecycle workflows, cognitive performance metrics, and data-mining capabilities. Participate in SAFe Agile delivery, author user stories, support DevSecOps integration, resolve technical impediments, and contribute to transition and knowledge-transfer materials. The role requires collaboration with architecture, product, and government teams in a cleared defense environment.

Develop and productionize AI/ML solutions for healthcare, focusing on multimodal LLMs, conversational AI, document understanding, search, and Q&A. Responsibilities include evaluating and fine-tuning models, building training data pipelines, managing the full ML lifecycle, deploying scalable solutions, troubleshooting integrations, documenting architecture, and ensuring HIPAA compliance. The role requires strong Python and PyTorch skills, experience with LLM training platforms and cloud ML services, and close collaboration in a fast-paced startup environment.

Leads an ML engineering team while designing, productionizing, and deploying AI systems for document understanding, search, Q&A, GenAI, virtual agents, NLP, NLG, and multimodal analysis. Oversees the ML lifecycle, APIs, code reviews, testing, troubleshooting, documentation, and healthcare data security. Manages projects, team performance, professional development, and cross-functional collaboration while ensuring HIPAA compliance and reliable, high-performance solutions.

Design, develop, and productionize machine learning and generative AI systems for document understanding, search, Q&A, virtual agents, and RAG applications. Build Python backend services, APIs, multi-agent architectures, retrieval infrastructure, and CI/CD pipelines. Manage the full ML lifecycle, evaluation, testing, telemetry, deployment, and troubleshooting while collaborating with cross-functional teams and maintaining healthcare data security and HIPAA compliance.

Design, develop, and productionize machine learning systems for document understanding, search, Q&A, GenAI, virtual agents, and RAG applications. Build Python backend services, AI APIs, multi-agent architectures, retrieval infrastructure, and CI/CD pipelines. Manage the full ML lifecycle, including training, validation, deployment, telemetry, A/B testing, and automated evaluation. Collaborate cross-functionally while ensuring scalable, secure, HIPAA-compliant healthcare solutions.

Develop and productionize applied AI/ML solutions for healthcare, including multimodal LLMs, document understanding, search, Q&A, and conversational AI. Responsibilities include evaluating and fine-tuning open-weight models, building ML data pipelines, managing model lifecycles, troubleshooting deployments, ensuring HIPAA compliance, and driving technical innovation across the team.