Top Machine Learning Jobs

The Senior Staff Machine Learning Engineer will improve Coupang's search and discovery systems using advanced algorithms, addressing bottlenecks, proposing solutions, and leading projects involving personalization and recommendations.

The Staff Machine Learning Engineer will lead AI initiatives, architect LLM systems, design evaluation frameworks, and mentor engineers.

The Machine Learning Engineer will conduct data analysis, develop ML applications, manage data transformations, and support API and ETL implementations.

As a Senior Machine Learning Engineer, you will enhance diffusion models, conduct research, collaborate on system implementations, and communicate findings effectively.

The Machine Learning & AI Engineer develops and deploys ML and AI systems for the Energy Industry, focusing on data quality and interpretability. Responsibilities include model design, pipeline development, AI agent systems, and collaborating with cross-functional teams.

Design, build, and deploy machine learning systems for controlling quantum processors. Develop reinforcement learning and Bayesian methods for QEC calibration, and work cross-functionally with product and R&D teams.

Design and implement ML algorithms for bidding and ranking systems in ads. Own complete ML systems from conceptualization to production. Collaborate with teams to integrate models and enhance advertising value using AI.

Develop and validate ML and statistical models on manufacturing, equipment, metrology, and yield data to improve process control, detect excursions, predict yield, and support deployment of analytics in production. Collaborate with engineers and stakeholders, build proofs-of-concept, dashboards, and automated tools while ensuring data quality and manufacturing relevance.

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 and scale Astronomers context layer for data: design retrieval and semantic search systems, experiment with LLMs and embeddings, turn research into product features, and influence architecture for AI-driven data tools used across organizations.

Design, train, and deploy ML models and pipelines to optimize Databricks serverless compute. Build end-to-end ML4Sys solutions, scale infrastructure, implement optimization and scheduling algorithms, and collaborate on product roadmap to improve performance and cost efficiency.

Support faculty and student researchers by designing and implementing an ML/LLM-based EduCore framework, developing research applications and testbeds, implementing edge-cloud cybersecurity strategies, organizing workshops and training, contributing to grant proposals, and performing data analysis and interpretation.

Lead and grow a team of ML and software engineers to build large-scale indexing, retrieval, and ranking systems for Snapchat ads. Define architecture and evaluation frameworks, collaborate with product and data science, measure outcomes, and raise engineering and ML standards while shaping Snap's broader ML strategy.

Design, build, and productionize causal ML models (uplift, heterogeneous treatment effects), analyze A/B tests and quasi-experiments, collaborate with product and engineering, ensure scalable, maintainable infrastructure, and balance model complexity with interpretability and business impact.

Develop and evaluate novel AI/ML solutions for healthcare using structured and unstructured data. Build and test deep learning and classical ML models, design experiments, perform statistical analysis and visualizations, document models, collaborate with stakeholders, RUAI, and engineering to productionize solutions and MLOps pipelines, and publish/patent research.

Lead end-to-end design, implementation, deployment, and operation of production AI/ML-enabled full-stack applications. Build LLM-powered features, design scalable backend services and data models, deploy on Azure, implement CI/CD and observability, and mentor engineers while ensuring secure, compliant integration into clinical workflows.

Lead development and production of underwriting and credit decisioning models across Cash App Borrow and Afterpay. Own full modeling lifecycle: problem formulation, feature engineering, training, calibration, experimentation, deployment, monitoring, and iteration. Build decision frameworks, agentic engineering workflows, and collaborate with cross-functional partners to align model behavior with business and regulatory goals.

Design, build, and operate scalable, low-latency search and retrieval systems powering Atlassian products. Develop and serve production ML models (neural rankers, embeddings, rerankers) using PyTorch and Triton. Implement vector indexing and RAG/agentic retrieval pipelines, ensure observability and cost optimization, manage on-call/runbooks, and collaborate cross-functionally while mentoring junior engineers.

Build and productionize statistical and ML models to optimize advertiser performance and business goals in mobile ad tech. Tune optimization, model competition and dynamic environments, design experiments, develop budget retention/growth and bidding systems, and collaborate across engineering and business teams while ensuring explainable, well-tested solutions.

The AI/ML Engineer designs and develops AI solutions, optimizes models, manages data flow for deployment, and conducts data analysis to enhance operational outcomes for mission operations.

As a Senior Machine Learning Engineer, you will manage the entire lifecycle of safety-critical ML perception models, optimize performance, and ensure reliable deployment on embedded systems.

The Lead Machine Learning Engineer will design, build, and productionize ML applications, oversee model performance, collaborate with teams, and ensure reliable deployment of ML systems at scale.

Design, implement, and deploy AI/ML models and analytics pipelines for intelligence customers. Analyze large, noisy datasets, build scalable backend services and APIs, and create visualizations and UIs. Collaborate with researchers, engineers, and end users to prototype, harden, and operationalize machine learning solutions across cloud, on‑prem, and edge platforms.

Develop, evaluate, and deploy AI/ML models for national security applications. Analyze complex datasets, build scalable backend services and APIs, create visualizations and UIs, and collaborate with researchers, engineers, and customers to transition prototypes into production across cloud, on-prem, and edge environments.

Contribute to development, deployment, and monitoring of production ML models; perform data cleaning, analysis, and visualization; collaborate with cross-functional teams to deliver AI-driven business solutions and communicate insights to stakeholders.