Top Machine Learning Jobs in Albuquerque, NM

Analyzes complex healthcare data to identify trends, develop statistical and machine learning methodologies, and produce actionable insights. Builds dashboards and reports, supports product and business decisions, promotes data literacy, and collaborates with cross-functional teams. The role also ensures analytics practices comply with healthcare privacy, governance, security, and regulatory requirements, including HIPAA.

Leads commercial growth execution as the CCO’s operating partner, orchestrating go-to-market programs across Sales, Marketing, Customer Success, Partnerships, and Operations. Establishes execution frameworks, operating rhythms, accountability systems, KPI dashboards, and performance reporting. Aligns cross-functional leadership, scales sales motions, identifies capability gaps, removes execution barriers, and drives measurable revenue, customer, and market growth initiatives.

Owns product vision, strategy, roadmap, and execution for Yahoo Search monetization across traditional and AI-powered search. The role optimizes sponsored search relevance, ranking, placement, coverage, revenue, and user experience while developing new advertising formats for conversational and generative experiences. Responsibilities include experimentation, marketplace analysis, performance measurement, partner collaboration, and close work with engineering, data science, analytics, and design teams.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Lead end-to-end data science and AI projects: translate business problems, prepare large datasets, build and deploy predictive/prescriptive and generative AI models (including LLM fine-tuning and prompt engineering), communicate insights to stakeholders, and promote responsible, explainable AI practices across consulting engagements.

Design and implement enterprise AI architectures spanning GenAI, machine learning, RAG, agentic workflows, data pipelines, APIs, and production deployments. Build Python prototypes and AI services, define governance and evaluation frameworks, integrate models with enterprise platforms, and guide engineering teams through implementation. The role requires hands-on expertise with LLM applications, orchestration frameworks, model lifecycle management, monitoring, security, and scalable production AI systems, preferably in financial services.

Design, build, and maintain optimization technology, full-stack systems, and data-intensive scheduling infrastructure. Improve optimization algorithms using linear optimization, machine learning, and generative AI. Lead complex technical initiatives, establish testing and observability, support scalable operations, troubleshoot customer issues, participate in on-call rotations, and mentor engineers while partnering closely with product and engineering teams.

Develop and validate machine-learning models for drug discovery using chemical, biological, imaging, phenotypic, and structural datasets. Perform exploratory analysis, integrate heterogeneous data, build reproducible workflows, and communicate findings to medicinal chemists, biologists, and research scientists. Apply supervised, deep-learning, graph-based, and ensemble methods to support compound optimization, target discovery, screening, and translational research.

Own sales analytics and forecasting as the trusted source of truth for sales leadership. Build predictive and machine learning models to assess deal close likelihood and slippage, maintain Salesforce and Tableau dashboards, improve data governance, and translate complex business questions into actionable GTM recommendations. Partner with Sales, Marketing, and Finance on reporting standards, strategic planning, QBRs, and executive insights.

Build and deploy production generative AI and machine learning systems for utility clients. Own engagements end to end, including discovery, architecture, retrieval, model selection, evaluation, deployment, monitoring, operator-facing applications, and post-go-live support. Integrate with client data and business systems, establish AI safety and governance controls, manage scope and risk, and communicate with technical teams and executives. Contribute reusable accelerators, evaluation frameworks, and reference architectures across client engagements.

Own Autopilot, Blend’s AI agent product for mortgage and lending operations, as a business and product roadmap. Prioritize capabilities, define evaluation and quality frameworks, establish pricing and packaging, productize customer-specific solutions, and measure business impact. Partner with AI engineering, forward-deployed teams, customers, and go-to-market leaders to drive adoption, ensure compliance and auditability, and translate production learnings into scalable offerings.

Leads strategic partnerships and new ventures from initial concept through signed agreement and scaled commercial launch. Identifies whitespace opportunities, develops business cases, structures and negotiates complex enterprise deals, builds financial models, and presents propositions to executive stakeholders. Coordinates Finance, Legal, Engineering, Product, Operations, and Commercial teams to validate economics, technical feasibility, funding, resources, and execution plans. The role is remote across the US and Canada with regular travel.

Leads product strategy, roadmap, and execution for threat intelligence, threat hunting, and detection capabilities. Partners with engineering, architecture, security research, executive, sales, and SOC teams to drive innovation, product launches, OKRs, portfolio prioritization, and customer security outcomes. Requires deep cybersecurity expertise and extensive senior product management experience, with strong knowledge of emerging threats, AI applications, and enterprise security products.

Leads strategy and execution for data products powering threat intelligence solutions. Uses customer insights, big-data analysis, and competitive research to prioritize opportunities, refine API-first products, and integrate data into UI, AI, and third-party workflows. Collaborates with engineering, marketing, sales, and solutions engineering from ideation through launch, influencing stakeholders and executives through data-driven analysis. Requires substantial cybersecurity or data product management experience and expertise in threat intelligence, APIs, analytics, and customer ecosystems.

Leads ExtraHop’s product marketing function, including team leadership, portfolio positioning, messaging, product launches, competitive analysis, sales enablement, content development, integrated campaigns, and executive-level customer and industry engagement. The role requires deep cybersecurity and enterprise technology expertise, strong storytelling and technical communication skills, and collaboration across product, sales, marketing, and partner teams.

Build and lead Precision Neuroscience’s partnership function across the life sciences and healthcare ecosystem. The role owns strategic partnership pipelines, structures and negotiates complex technology and scientific collaboration agreements, advises leadership on deal strategy, translates technical work into commercial narratives, and establishes partnership processes for an early-stage neurotechnology company.

Manages Pulmovant’s enterprise application portfolio, including validated GxP and clinical systems and core IT platforms. Oversees system performance, configuration, compliance, access, documentation, integrations, upgrades, and validation. Partners with Clinical, Regulatory, Quality, Finance, and HR stakeholders, manages vendors and an applications team, and leads continuous improvement, root-cause analysis, and application initiatives. The role also supports AI and machine learning governance, model validation, and responsible data practices.

Build and deploy AI applications, agents, intelligent workflows, document-processing solutions, predictive analytics, and automation. The role spans AI use-case assessment, solution design, prototyping, evaluation, testing, production deployment, monitoring, governance, and responsible-AI controls. The engineer will integrate AI with enterprise systems, APIs, workflows, and data platforms while partnering with engineering, cybersecurity, governance, and mission teams in a federal environment.

Own a broad territory focused on acquiring net-new enterprise customers for SingleStore’s AI and data platform. Build self-sourced pipeline through outbound, account-based, partner, and market-education motions; engage executive and technical stakeholders; develop ROI and TCO cases; navigate complex technical, security, legal, procurement, and negotiation processes; partner with engineers to establish production workloads; and manage forecasting, qualification, CRM hygiene, and new-logo performance.

Lead enterprise AI security architecture, strategy, governance, and secure-by-design implementation across cloud, hybrid infrastructure, software, and AI/ML environments. Design security patterns for LLM hosting, agentic systems, vector databases, and inference endpoints; translate regulatory requirements into technical standards; review complex architectures; implement AI security guardrails and policy enforcement; advise engineering and product teams; and mentor teams while influencing senior stakeholders.

Drives growth by identifying strategic partnerships with enterprise AI and machine learning companies. Develops business development strategies, builds relationships with senior decision-makers, negotiates complex deals, and collaborates with internal teams to implement partnerships. Requires strong AI/ML industry knowledge, executive communication skills, strategic thinking, and adaptability in a fast-paced startup environment.

Leads cross-functional programs that improve products, customer support, and value delivery. Owns execution, coordinates engineering and operations teams, establishes scalable processes, uses data-driven insights to improve customer models, scopes and sequences major initiatives, and communicates progress to stakeholders. The role requires program management experience, strong communication and influence skills, comfort with ambiguity, rapid execution in scaling environments, and interest in AI systems.

Manage customer relationships and AI-focused engagements, serving as a trusted advisor and primary point of contact. Oversee data projects, ensure SLA and delivery goals, coordinate with engineering and operations teams, address client concerns, and translate technical AI and machine learning requirements into actionable project plans.

Conduct hands-on research with frontier AI labs to evaluate model behavior, identify failure modes, and design interventions that improve reliability. Responsibilities include designing evaluations, building data pipelines and fine-tuning workflows, collaborating with internal and external stakeholders, and addressing challenges involving long-context reasoning, tool use, and real-time feedback loops.

Manages strategic client accounts for an AI data platform, serving as the primary customer contact and trusted advisor. Oversees data projects, SLAs, quality, and deliverables while coordinating engineering, operations, and project teams. Builds strong client relationships, addresses concerns, translates AI and NLP requirements into actionable plans, and ensures solutions meet customer goals.