Top AI & Machine Learning Jobs

Leads the end-to-end execution layer for machine learning, including data pipelines, large-model training and fine-tuning, evaluation, inference, deployment, monitoring, and reliability. Designs scalable GPU-based systems, optimizes performance and cost, partners with research and application engineering, and translates model capabilities into measurable production improvements. Also provides technical leadership, judgment, and support for high-impact ML delivery.

Lead application engineering for an AI-powered email product across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, make architectural trade-offs, establish observability and operational excellence, and lead a small team of senior engineers through hands-on technical mentorship and code development.

Lead hands-on engineering for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Define APIs and abstractions, make architecture and scalability decisions, improve observability and reliability, and lead a small team of senior engineers through technical reviews, mentorship, and production delivery.

Lead and architect AI transformations at Broadcom, overseeing large-scale projects and serving as a technical authority on AI systems, ensuring innovative, scalable solutions.

Support pilots of AI tools, build Power BI dashboards, research AI use cases for beauty retail, automate repetitive processes with no-code/low-code solutions, analyze customer and product trends, and present findings to cross-functional teams and leadership.

Lead zero-to-one product development for AI-native web applications: shape product and technical direction, ship full-stack prototypes rapidly, integrate foundation-model APIs and RAG, collaborate cross-functionally, make architecture and tooling decisions, and iterate from user feedback toward venture-level outcomes.

Develop and maintain predictive pricing, retention, and conversion models using GLMs and advanced analytics; build monitoring tools for rating performance; support rate filings, product pricing analyses, competitor research, and cross-functional collaboration to inform pricing and enterprise risk decisions.

The AI Engineer will research and develop AI/ML models, particularly LLMs, and collaborate in R&D to create innovative communication solutions.

Design, develop, and deploy production-grade machine learning models and pipelines. Preprocess and analyze large datasets, collaborate with cross-functional teams, monitor model performance, and implement scalable ML solutions to improve customer experiences.

Define the strategic vision for machine learning initiatives, lead development of advanced models and agentic systems, build scalable ML pipelines, deploy and monitor production solutions, and collaborate across engineering, data science, and product teams. The role requires designing highly reliable platforms handling millions of requests, mentoring technical staff, contributing research, and integrating AI capabilities with products and payment systems.

Lead the strategic vision, development, optimization, and production deployment of advanced machine learning models and pipelines. Responsibilities include large-scale data preprocessing, model monitoring, platform and framework development, cross-functional product integration, research publication, and mentoring engineers and data scientists. The role emphasizes recommendation systems, search ranking, personalization, graph-based ML, social platform applications, feature stores, model serving, experimentation, and business-impact metrics.

The Developer Relations Manager will engage research labs to promote NVIDIA's AI platforms, assess institutional opportunities, provide technical guidance, and connect with researchers to drive product development and collaboration.

Build and deploy AI-powered tools for GeForce NOW SRE operations, transforming production signals, metrics, logs, and other data into actionable intelligence. Develop LLM- and agent-based systems for automated incident root-cause analysis and service trend prediction. Own large-scale data workflows, LLM pipelines, monitoring, cloud deployments, and AI framework architecture decisions using Kubernetes and AWS.

Develop datasets, models, and evaluation systems for content safety, product security, robustness, and ML fairness for multimodal LLMs. Research and implement bias detection/mitigation, define safety metrics, contribute MLOps automation and safety tooling, and collaborate with engineers, researchers, and data scientists to improve scalable model safety and inclusivity.

Analyzes large-scale datasets, defines business metrics and OKRs, investigates trends and root causes, develops predictive and forecasting models, creates dashboards and visualizations, conducts experiments, and communicates actionable recommendations to technical and non-technical stakeholders. The role also includes internal consulting practice collaboration, thought leadership, case studies, and digital transformation content development.

The Senior Machine Learning Engineer at Grailed will enhance personalization and recommendations, develop AI/ML solutions, and collaborate across departments for data-driven insights.