Top Machine Learning Jobs in Albuquerque, NM

Drive end-to-end ML model inference performance: build kernel- and system-level performance models, optimize kernel microcode and compiler algorithms, debug runtime performance on system and cluster, and develop tooling to visualize and analyze performance data from the Wafer Scale Engine and compute cluster.

Build and maintain data and model layers for an AI decision‑support system: data ingestion, probabilistic record matching, calibrated scoring, provenance‑tracked retrieval and grounded generation, and secure production pipelines. Ensure explainability, auditability, and accredited deployment in a controlled government cloud environment.

Design and build data and model layers for an accredited AI decision-support system: record matching, calibrated scoring, provenance-tracked retrieval and generation, secure ingestion pipelines, model packaging/serving, and instrumentation for audit and accreditation.

Build and operate model validation, quantization, and safety systems for production ML. Develop pipelines for security scanning, optimization (quantization/pruning), routing, prompt management, and evaluation frameworks measuring accuracy, performance, and safety across model lifecycles.

Lead design, development, and production deployment of ML models and GenAI/LLM solutions to optimize clinical trial workflows. Build scalable pipelines, monitoring, and CI/CD for robust production use. Collaborate cross-functionally, mentor junior engineers, and communicate technical results to technical and non-technical stakeholders.

The role involves product management of AI tools, collaborating with research, engineering, and design teams to develop and enhance products, focusing on user needs and market value.

Build and productionize LLM-powered tools, RAG systems, and agentic workflows. Deploy and operate AI infrastructure on Kubernetes, manage model serving and vector stores, integrate identity and access controls, automate CI/CD and data pipelines, and establish evaluation and observability for deployed services in a defense-oriented environment.

Lead a 4-6 engineer team owning the internal data warehouse and end-to-end ML Ops lifecycle: experimentation, training pipelines, deployment, and monitoring. Provide technical leadership, collaborate with cross-functional partners, define platform standards, and coach engineers to deliver reliable, scalable data and ML infrastructure that powers product analytics and identification features.

Lead and grow a multidisciplinary ML team focused on identification accuracy. Own roadmap, drive model training, evaluation, and deployment at scale, and partner cross-functionally to translate customer needs into technical priorities and measurable model performance outcomes.

Build and prototype supervised ML models to predict ad campaign delivery outcomes (reach, frequency, contention). Own feature engineering, evaluation frameworks, interpretability, and partner with ML engineers to deploy and monitor models at scale. Collaborate across product, engineering, and sales to drive adoption and measure lift versus simulation baselines.

Lead the design and delivery of a production-grade localization ML stack for autonomous vehicles: architect models for landmark-based localization across sensors, own data and evaluation pipelines, drive feature development to deployment, define failure modes and safety coverage, and mentor engineers while integrating cutting-edge multimodal and foundation-model techniques into robust, real-time localization systems.

Design, build, and operate production agentic AI systems on AWS Bedrock AgentCore using Strands/LangGraph. Implement tool integrations (MCP/Gateway), evaluation pipelines and LLM-as-judge CI gates, observability, cost/latency controls, and guardrails for agents operating on live dealer data.

Design, implement, and own a reusable on-device ML inference platform for resource-constrained devices. Integrate inference into RTOS firmware, develop drivers and DMA paths, debug on real hardware, train and optimize models for microcontroller-class hardware, and ship features to production while raising team expertise and using AI-assisted tooling.

Design, develop, implement, and monitor statistical and machine learning models to support credit risk, collections, servicing, marketing, and forecasting. Build data pipelines, translate business needs into analytical solutions, support model governance, testing, deployment, and ongoing performance monitoring, and communicate results to stakeholders.

Lead development and deployment of predictive and ML models for credit risk, collections, and operations. Mentor data scientists, promote MLOps and modern tools, present insights to stakeholders, and drive experimentation with techniques such as causal inference and GenAI.

Partner with customers to design and deliver production-ready AI/ML solutions, drive POC-to-production success, troubleshoot technical issues, inform product roadmap with customer feedback, and communicate outcomes to stakeholders.

The Senior Data Engineer will own the data strategy, managing sourcing, structuring, and optimizing multimodal data to enhance AI models and products.

As a Senior Machine Learning Engineer, you will develop and deploy AI-driven products to enhance clinical trials, collaborating with teams to improve patient care through scalable machine learning solutions.

The Senior Principal AI/ML Architect will drive AI architectural vision, influence multi-squad strategy, design intelligent systems, and set operational standards for Yahoo Mail's AI capabilities.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

The Staff Machine Learning Engineer will develop and maintain MLOps tools, optimize model performance, and collaborate on ML system architecture with a focus on enhancing developer experience.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

Lead end-to-end development of fraud and risk ML systems: define problems, engineer features, build and evaluate models, collaborate with product and engineering, deploy with ML engineers, and monitor production performance to detect drift and trigger retraining.