Top Remote Machine Learning Jobs in Albuquerque, NM

Leads the vision, roadmap, and development of an AI-enabled Service Catalog platform. Combines product leadership with hands-on full-stack engineering across web applications, APIs, integrations, workflows, and AI features. Manages engineering and product strategy, defines success metrics, partners with architects and stakeholders, prototypes capabilities, and ensures scalable, secure, responsible solutions with human oversight.

Lead healthcare and pharmaceutical marketing analytics: analyze CRM, media, healthcare and behavioral data; build segmentation, attribution, audience scoring, and predictive models; develop measurement frameworks; use Python, SQL, BI and automation; translate insights for executives; mentor juniors and partner with Strategy, Media, and Client Services.

Conduct applied research on scalable optimization and resource-allocation algorithms for large communication networks. Apply machine learning, reinforcement learning, integer optimization, graph-based methods, and metaheuristics to improve solution quality and efficiency. Collaborate with software engineers to integrate algorithms into production aerospace networking systems, evaluate performance at realistic scale, publish research, support patent applications, and document novel methods.

Build and maintain computer vision and machine learning pipelines for automated device inspection, grading, and quality assessment. Responsibilities include data preparation, feature engineering, model training and evaluation, production Python development, monitoring, annotation workflows, documentation, code reviews, and collaboration with product and operations teams. The role supports the full ML lifecycle and offers increasing ownership under senior engineering guidance.

Manage the operational integrity of an automated group health insurance underwriting workflow. Review and approve RFPs, validate broker and external data, correct errors, monitor funnel and risk metrics, analyze quote-to-bind performance, triage inquiries, coordinate with sales and health plan operations, and maintain underwriting dashboards and reporting.

Own product strategy, roadmap, problem definition, prioritization, and outcomes for a core platform area. Partner with engineering, data science, Product Owners, Product Marketing, sales, clients, and leadership to deliver data- and AI-driven capabilities. Define business cases, requirements, success measures, and go-to-market support while incorporating customer feedback, adoption data, regulatory considerations, and compliance requirements.

Lead technical strategy and architecture for Azure Storage geo-replication, data protection, failover orchestration, and business continuity. Drive cross-team delivery, operational excellence, telemetry, automation, AI-assisted operations, and represent the team in executive and customer engagements.

Build proof-of-concept AI agents, RAG pipelines, and intelligent workflows for internal engineering operations. The intern will automate incident analysis and code review summaries, evaluate AI coding and monitoring tools, collaborate cross-functionally to improve efficiency, and follow security-focused data hygiene and secrets management practices.

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.

Build and maintain high-performance, reliable backend systems for Yahoo Mail AntiSpam and Delivery. Design and develop complex server applications on Unix, leverage cloud platforms (GCP/AWS), containerization (Docker/Kubernetes), and ML/AI models to detect and deliver messages. Troubleshoot, optimize performance, and collaborate cross-functionally to ensure scalability and uptime.

Lead product marketing for Temporal’s AI initiatives, owning positioning, messaging, competitive intelligence, launches, GTM strategy, content, sales enablement, and market insights. Translate complex AI infrastructure, workflow orchestration, agent, LLM, and human-in-the-loop capabilities into compelling value for technical and business audiences. Partner with product, engineering, sales, demand generation, developer relations, and communications to drive adoption and shape product direction.

The Cloud Inference Engineer is responsible for building and maintaining a scalable LLM inference platform, focusing on distributed systems and machine learning technologies while collaborating across teams to optimize performance.

Leads enterprise-wide AI strategy, governance, portfolio execution, ROI tracking, and responsible AI across multiple healthcare business units. Chairs AI governance bodies, manages initiative gating and investment decisions, coordinates clinical, operational, financial, vendor, compliance, and technology stakeholders, and drives measurable transformation outcomes. The role also leads AI talent development, organizational adoption, vendor discipline, clinical safety, regulatory readiness, and continuous improvement of the governance operating model.

Lead strategy and roadmap for Brain Health & Detection, drive AI/ML-powered detection and anomaly systems, partner with engineering and data science, set success metrics, and lead end-to-end product execution and adoption for scalable service health intelligence.

Leads AI/ML engineering teams across platform and delivery pods, managing employees and contractors while enforcing code, agent, review, and production-readiness standards. Partners on reusable templates, pipelines, and evaluation processes, ensures adoption across teams, manages contractor performance and conversion, and removes delivery blockers. The role requires hands-on credibility reviewing agent architectures, prompts, and evaluation suites, with experience operating as a player-coach in fast-moving organizations.

Owns the lifecycle of AI-powered analytics products supporting clinicians, from requirements and discovery through production and maintenance. Partners with data scientists, engineers, clinicians, and executives to define roadmaps, prioritize trade-offs, establish data contracts and SLAs, validate model results, track performance, and demonstrate ROI. Leads Agile scrum teams and drives cross-functional delivery in complex healthcare environments.

The Product Marketing Manager will execute product launches and support go-to-market initiatives, collaborating with cross-functional teams to develop and deliver messaging and enablement materials targeting both technical and business stakeholders.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Build and operate production AI capabilities across enterprise platforms, including agent runtimes, RAG, model routing, tool calling, guardrails, evaluation, monitoring, and cost optimization. Ship reliable, safe, measurable AI features; develop evaluation frameworks and quality controls; monitor production behavior; and collaborate with client-facing product, engineering, platform, backend, and security teams to deliver practical AI solutions.

Own LanceDB’s product direction by shaping the roadmap with technical leadership, researching users and competitors, gathering continuous user insight, and driving cross-functional prioritization. The role partners closely with engineering as a technically respected peer, defines product changes for AI and data-platform users, uses metrics and experiments to guide decisions, and operates with high ownership in an ambiguous environment.

Build and deploy generative AI solutions for creative enterprise workflows across image, video, 3D, vector graphics, and design. Partner with customers to identify opportunities, prototype integrations, adapt multimodal models, integrate AI into production systems, diagnose technical issues, evaluate solution quality, and develop reusable deployment patterns. Collaborate with research, product, engineering, and design teams to translate customer needs into scalable technical solutions.

Leads and develops a team of Partner Account Managers while driving partner revenue and strategic growth. Responsibilities include hiring, coaching, territory management, forecasting, Salesforce activity tracking, sales planning, customer and partner engagement, trade show participation, product expertise, and competitive market analysis. The role collaborates with senior leadership and cross-functional teams to achieve sales goals, strengthen customer relationships, and expand business accounts.

Lead the architecture and implementation of AWS cloud-native platforms for fraud detection, risk analysis, and digital intelligence. Design scalable, resilient, secure, low-latency REST and event-driven systems using Java, Kubernetes, Kafka, and SQL/NoSQL databases. Set technical strategy and architecture standards, mentor engineers, conduct reviews, and guide multiple teams. Partner with product, data science, security, and business leaders to embed AI and machine learning capabilities, while improving delivery, observability, compliance, and cost efficiency.

Advise enterprise clients on AI and data strategies, transformation roadmaps, operating models, governance, privacy, technology rationalization, and business cases. Lead discovery workshops, executive engagements, and art-of-the-possible sessions; prioritize use cases and connect technical recommendations to business outcomes. Build the advisory practice, develop offerings, manage multidisciplinary teams, mentor staff, strengthen client and partner relationships, and support successful distributed project delivery.

Build production-ready AI applications, services, APIs, reusable frameworks, and autonomous agents for enterprise quality engineering. Lead architecture and complex technical initiatives, integrate AI into software development and testing workflows, evaluate emerging technologies, and establish AI governance, security, observability, and development best practices. Partner across engineering, product, security, architecture, and data teams while mentoring engineers and shaping long-term AI strategy.