Top Machine Learning Jobs

Leads the AI strategy and architecture for supply-chain planning operations. Builds production machine-learning, data-stitching, anomaly-detection, scenario-modeling, alerting, and agentic workflow capabilities across enterprise planning systems. Establishes MLOps, governance, observability, security, and explainability practices while partnering with planners, engineering, manufacturing, procurement, finance, and executives to deliver trusted operational decision support.

Design, develop, and operate scalable VoIP infrastructure and telephony platforms supporting AI-driven voice workloads. Integrate SIP/RTP systems, LLMs, and real-time communications while improving observability, reliability, deployment, and SRE practices. Build tested, reusable software across telephony and AI integration layers, collaborate with product and infrastructure teams, support production systems, and mentor colleagues across telecom and AI engineering.

Develop and operate scalable VoIP infrastructure supporting AI-driven voice workloads. Build telephony platforms, observability features, and LLM integrations across SIP/RTP, Kamailio, RTPEngine, and related systems. Collaborate with engineering, product, infrastructure, and SRE teams on deployments, production support, automation, testing, and platform reliability. Mentor colleagues and promote engineering best practices across telecom and AI disciplines.

Create, solve, review, and validate coding-heavy machine learning tasks for AI training. Build and optimize models, training and inference workflows, data pipelines, evaluation systems, and reproducible Python workflows. Diagnose numerical and system-level failures, improve memory use and throughput, and verify technical correctness while working with coding agents.

Leads the technical strategy, architecture, and roadmap for scalable fraud detection platforms. Develops reusable rules, machine learning, graph analytics, risk-scoring, APIs, and detection infrastructure for customer use. Partners across engineering, data science, security, risk, product, and customer teams to translate fraud patterns into production capabilities. Oversees detection metrics, root-cause analysis, system resilience, real-time processing, and adaptive feedback loops while managing complex technical initiatives and engineering organizations.

Develop AI-driven software for VLSI and circuit design automation. Research electronic design automation techniques, build agentic AI solutions, analyze datasets, validate hypotheses, and create machine learning models and algorithms to optimize circuit and layout design outcomes. Collaborate across engineering and research teams on pre-silicon and post-silicon design, circuit optimization, and SPICE correlation.

Develop AI-driven software systems for VLSI and circuit design, including machine learning models, combinatorial optimization algorithms, and agentic AI workflows. Analyze datasets, validate hypotheses, and build algorithms for circuit and layout optimization, pre- and post-silicon design, and SPICE correlation. Collaborate across engineering and research teams while advancing electronic design automation solutions.

Lead the architecture and development direction of NVIDIA ALCHEMI and related AI software for chemistry and materials science. Design developer tools, APIs, and workflows; translate advances in machine learning, generative modeling, interatomic potentials, and quantum chemistry into product capabilities; coordinate researchers, engineers, product teams, partners, and customers; and represent NVIDIA at scientific and industry events.

Lead AI/ML initiatives for Air Force condition-based maintenance, from predictive model development through production deployment. Architect data pipelines, develop machine learning, deep learning, NLP, and generative AI solutions, and build knowledge graph capabilities. Provide technical leadership, integrate models into applications and workflows, and monitor data quality, model performance, drift, and bias. Collaborate with engineering, cloud, software, and product teams while supporting defense aviation logistics and sustainment missions.

Develop visual reasoning, 3D spatial understanding, and embodied intelligence capabilities for robotics foundation models. Design scalable robotics data pipelines covering collection, teleoperation, annotation, quality assessment, and evaluation. Research multimodal foundation model approaches for robotic planning, reasoning, and decision-making, including VLA architectures. Collaborate with foundation model researchers and robotics partners to evaluate capabilities and address real-world deployment challenges.

Conduct research and develop machine learning, generative AI, stochastic simulation, and optimization solutions for clinical trial operations. Build predictive models, optimization engines, LLM workflows, and data pipelines supporting enrollment forecasting, cost optimization, site selection, document processing, and risk management. Validate models, promote responsible AI and data quality, contribute reusable code and documentation, and communicate findings to technical and clinical stakeholders.

Master’s machine learning engineering intern developing, training, improving, and deploying large-scale production ML systems. Projects may support fraud detection, user behavior prediction, foundation models, and enhanced customer support. The role involves end-to-end model development, collaboration across teams, learning unfamiliar systems, and communicating progress effectively.

Lead full-stack development and technical strategy for Copilot Discover: design, build, and operate scalable ML/deep-learning models and prompt strategies; implement responsive TypeScript/React/NextJS/Web Components front-ends; develop REST APIs and data layers; ensure performance, scalability, and reliability; collaborate across UX, backend, and ML teams; participate in code reviews and architecture decisions.

Build, deploy, and maintain machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Use AI tools to develop scalable services while ensuring code correctness, security, performance, and architectural quality. Mentor collaborators and solve ambiguous, large-scale problems.

Build and deploy machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Develop scalable, secure, production-ready services using AI-assisted engineering workflows, while contributing to ranking, recommendations, search, content understanding, or image-generation applications.

Research and implement machine learning perception solutions for autonomous driving in weather and degraded-road conditions. Develop models for data-scarce scenarios, evaluate ML systems, and collaborate with onboard software, planning, and control teams. The role involves implementing algorithms from academic papers and potentially working with computer vision, computational geometry, real-time robotics, and vision-language models.

Designs, builds, deploys, and maintains production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, monitoring production models, and applying responsible AI, governance, and security practices. The role collaborates with Product, Data Science, and Agile engineering teams to deliver scalable ML solutions.

Develop and deploy production machine learning models and infrastructure at scale. Responsibilities include model development, data pipeline construction, cloud and Kubernetes operations, distributed data processing, CI/CD automation, model monitoring and retraining, responsible AI governance, and collaboration with product and data science teams. The role requires strong programming, machine learning, distributed systems, and production deployment expertise.

Develop and optimize machine learning, deep learning, computer vision, and image-processing algorithms for semiconductor inspection systems. Responsibilities include algorithm design from prototyping through production deployment, model training and validation, performance debugging, robustness improvements, and collaboration with software, systems, applications, and field engineering teams. The role also involves architectural contributions and optimization for scalability, maintainability, GPU acceleration, and real-time production environments.

Leads architecture and hands-on implementation of scalable AI Defense systems that detect, validate, and stop threats to AI applications, agents, and foundation models. Owns high-performance platform services, LLM inference and model-hosting strategy, secure enterprise deployments, and production delivery. Partners with Product, Threat Research, and Operations while mentoring engineers and guiding technical strategy across teams.

Leads a portfolio of biomedical AI research, develops multimodal models integrating imaging, EHR, wearable, and genetic data, and guides ML engineering and data science teams. Responsibilities include securing research funding, building collaborations, generating proof-of-concept results, fine-tuning foundation models and LLMs, evaluating models rigorously, mentoring technical teams, publishing research, and communicating results to federal sponsors and partners.

Six-month engineering internship on the SABRE Process Development team, applying data analytics, machine learning, automation, computer vision, predictive modeling, digital twins, and sensor integration to improve semiconductor electroplating equipment and processes. The intern will analyze experimental data, develop intelligent equipment capabilities, explore tool design concepts, and optimize manufacturing processes while working across hardware, software, and engineering disciplines.

AI Engineer Interns will preprocess and analyze data, support machine learning model development and optimization, help deploy AI solutions, document experiments and workflows, and research emerging AI methods. The role requires collaboration with senior engineers and cross-functional AI team members. Interns will work on ecommerce technology problems in a hybrid, full-time summer internship in Lehi, Utah, with potential for a future full-time role or part-time extension.

Design and develop high-scale distributed AI infrastructure services supporting model training and inference on Azure Machine Learning. Build backend services using C#, Go, Rust, and C++ on Kubernetes and Docker. Operate reliable, secure, low-latency systems with 24/7 availability, provide operational ownership, collaborate with engineers and researchers, and provide technical leadership.

Build and improve ML and LLM systems for CustomerLake personalization use cases, including recommendations, targeting, churn, and lifetime-value modeling. Develop production evaluation and monitoring frameworks tied to business outcomes, inspect model behavior, and advance novel methods. Partner with product, engineering, and design to create scalable solutions, establish ML/AI best practices, and lead ambiguous 0-to-1 initiatives from concept through launch and adoption.