Top Senior Machine Learning Jobs in Albuquerque, NM

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Build and deploy production-grade AI and machine learning solutions for marketing and sales, including LLM agents, data-quality pipelines, semantic search, RAG, analytics, and integrations with Salesforce, Marketo, and Adobe platforms. Define evaluation and governance practices, monitor AI services, implement privacy controls, and collaborate with platform teams. Train and mentor technical and business stakeholders on responsible AI adoption while supporting DevOps, incident response, and operational runbooks.

Leads technical pre-sales for supply chain integration solutions, partnering with Sales to conduct discovery, design ERP/WMS/TMS and trading-partner architectures, run POCs, deliver demonstrations, and respond to RFPs. Advises technical and executive stakeholders on business outcomes and AI opportunities, translates capabilities into measurable ROI, and collaborates with Product, Customer Success, and Professional Services on roadmap feedback and post-sale handoffs.

Lead product strategy, roadmap, and delivery for Falcon Complete’s internal MDR tooling, including AI-powered investigation and triage, ticketing, case management, and analyst workbench systems. Partner with security operations, analysts, engineering, and data science teams to translate workflows into product requirements, prioritize automation, improve analyst productivity and response times, define success metrics, and enhance customer outcomes.

Develop and implement data science strategies for machine learning applications including lead scoring, churn modeling, pricing, personalization, and fraud detection. Own end-to-end initiatives spanning data analysis, feature engineering, model training, inference, and monitoring. Collaborate with engineering, infrastructure, analytics, and business teams to deliver measurable impact, improve data capabilities, and communicate findings to technical and non-technical stakeholders.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Own the strategy, roadmap, requirements, launch, adoption, and success metrics for a technical platform. Partner with engineering, data, product, and business teams; evaluate architecture, APIs, and system tradeoffs; prioritize foundational investments against customer and revenue outcomes; and apply AI to improve platform capabilities. The role requires strong product management experience, technical depth, analytical skills, and production experience with LLM-based or ML-adjacent products.

Lead the In-Vehicle Experience team at Samsara, focusing on transforming in-cab cameras into intelligent companions. Collaborate with engineering, prioritize features, and ensure client interactions highlight trust and connection. Conduct customer research and integrate AI and ML into product features.

Develop and optimize full-funnel, multi-channel demand generation campaigns that generate pipeline, accelerate deals, and support cross-sell and upsell opportunities. Partner with product marketing, sales development, content, product, field, partner marketing, and analytics teams. Build lead-nurturing programs, align assets to buyer stages, monitor conversion and ROI metrics, and create dashboards to optimize campaign performance.

Lead go-to-market strategy for Sprout Social’s AI portfolio, including the Trellis AI agent. Develop enterprise launch playbooks, positioning, executive messaging, campaigns, and enablement programs. Analyze market and competitive dynamics, influence product roadmaps, improve launch processes, and partner with Product, Sales, Revenue, Brand, and executive leadership to drive adoption, revenue growth, deal expansion, and category leadership. The role also mentors marketing team members and operates with substantial autonomy.

Lead agile design and development projects for mobile apps and digital products. Coordinate product, design, engineering, marketing, leadership, and client stakeholders; manage project plans, scope, requirements, backlogs, acceptance criteria, deadlines, workflows, and deliverables. Support scoping and client relationship management while ensuring successful end-to-end software launches and alignment with business objectives.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Lead ecosystem-level data science and econometric analyses across Xbox, Game Pass, Console, PC, and Store experiences. Evaluate growth, engagement, monetization, experiments, incrementality, and investment ROI using statistical and causal inference methods. Build reusable analytical products and self-service capabilities, influence executive decisions, and mentor team members.

Own and advance Wonderlic’s AI-powered Jobs Engine, using production machine learning and NLP to analyze labor-market data and infer job characteristics at scale. Partner with product, engineering, and I-O psychology teams to integrate responsible AI into assessment and workforce solutions. Apply fairness, explainability, legal defensibility, and rigorous evaluation practices to employment-related AI systems, including generative AI outputs and assessment interpretation.

Leads client-facing management consulting and transformation programs spanning revenue operations, marketing, sales, customer growth, data, analytics, and AI. Advises senior executives, develops operating models and transformation roadmaps, leads solutioning and proposals, oversees delivery governance, and translates business needs into implementable data and technology solutions. Builds thought leadership, reusable consulting assets, and high-performing teams while supporting strategic growth and client relationships.

Leads and develops a fraud analyst team responsible for investigating emerging threats, responding to incidents, and improving fraud-risk strategies. Owns investigative standards, playbooks, workflows, and tools while serving as an escalation resource for complex cases. Partners with Data Science, Customer Success, Product, Engineering, and business leaders to convert fraud insights into scalable signals, model improvements, and better customer outcomes. Supports customer conversations as a fraud subject-matter expert and coaches analysts on communicating risk decisions.

Leads global Integrated Facilities Management service transitions from incumbent suppliers to Cushman & Wakefield. Responsibilities include developing transition strategies, managing executive stakeholders, identifying and mitigating risks, designing workflows and service matrices, coordinating recruiting, Finance and Procurement workstreams, preparing go-live tools, training incoming staff, and driving process improvements. The role also uses AI-driven analytics and machine learning insights for risk detection, forecasting, scenario planning, and workflow optimization.

Provides technical leadership for architecture and evolution of large-scale distributed data platforms supporting Azure network telemetry. Designs streaming and batch systems, establishes reusable engineering patterns, drives cross-team technical strategy, and owns capabilities from design through production operations. Ensures scalability, reliability, security, performance, data quality, and operational excellence while mentoring engineers and influencing technical leaders. The role also involves architectural reviews, dependency management, risk reduction, and applying AI or machine learning approaches to operational systems.

Leads enterprise AI solution strategy, architecture, pre-sales, and implementation delivery for customers across the Americas. Defines AI use cases, validates feasibility, designs scalable architectures, guides Professional Services teams, and resolves complex technical challenges. Develops AI service offerings, methodologies, reusable assets, and enablement programs while mentoring architects, consultants, and project leaders. Requires deep experience with enterprise transformations, LLMs, multi-agent systems, generative AI, data architecture, cloud platforms, and production AI deployments.

Design, scale, and optimize ClickUp's search infrastructure using OpenSearch/Elasticsearch. Improve relevance, speed, and real-time indexing, build vector search capabilities, create evaluation frameworks, and collaborate with AI/backend/product teams to integrate and troubleshoot search at scale.

Prototype and develop generative AI applications for business software, including automated model evaluation, synthetic video, and consistent image and brand asset generation. Own projects from experimentation through product delivery, combining system architecture, quantitative reasoning, product judgment, and creative LLM application development.

Lead analytics for client engagements across data onboarding, study planning, predictive modeling, production deployment, and ongoing optimization. Develop models for audience targeting, segmentation, propensity scoring, and campaign optimization, primarily for commercial pharmaceutical applications. Partner with business, analytics, and IT stakeholders, lead technical workshops, present findings to senior audiences, and manage client relationships. Build machine learning pipelines using Python, SQL, and cloud technologies, while contributing client insights to internal product development.

Develop and maintain Power BI reports, dashboards, visualizations, semantic models, and analytics solutions. Translate business requirements into scalable technical solutions, optimize queries and data models, ensure reporting accuracy and performance, and support data governance. Serve as the primary liaison for business users, provide training and troubleshooting, manage tickets, and collaborate with Data and QA teams. The role also advances AI and machine learning capabilities, mentors colleagues, and contributes to automation, documentation, and code reviews.