Top Machine Learning Jobs

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.

Build and deploy machine learning models for Snapchat products, contributing across data analysis, training, evaluation, and production deployment. Collaborate with cross-functional teams to launch scalable ML-powered features while maintaining high standards for code quality, security, and reliability. Apply established machine learning techniques to ranking, recommendations, search, content understanding, image generation, and related applications.

Build cloud-based AI and machine learning solutions for intelligent enterprise services. Collaborate with developers, product managers, product owners, and quality engineers to design, implement, test, and deliver scalable software. Apply AI/ML techniques, high-performance algorithms, object-oriented programming, modular design, unit testing, profiling, and code optimization to improve enterprise workflows and user experiences.

Leads machine learning and software engineering teams building models, agentic systems, data pipelines, and ML infrastructure for advertising integrity and recommendations. Owns technical strategy, roadmap, model quality, reliability, privacy, and responsible AI. Partners cross-functionally to deliver measurable outcomes, scales LLM-powered ad review, contributes technically, and hires and mentors high-performing engineers.

Develop production LLM-powered agents and machine learning solutions for customer risk identification and threat detection. Translate threat-research methods into agent logic, build data requirements and pipelines, evaluate precision and recall, backtest and tune models, and deploy and monitor reliable solutions using CI/CD. Collaborate with threat researchers and data engineers while addressing production reliability, observability, data quality, cost, latency, and accuracy.

Develop and deploy AI and analytics models for battery manufacturing quality, defect detection, yield, scrap, and throughput improvement. Analyze plant data from MES, historians, PLCs, OT systems, and inline metrology. Partner with process, controls, quality, operators, vendors, and central technologists to convert model insights into process changes, error-proofing, and standard work while sharing reusable improvements across plants.

Design, build, and operate scalable machine learning platform capabilities across feature engineering, model training, inference, monitoring, and lifecycle management. Develop reliable software and APIs, improve production reliability and performance, resolve technical issues, and establish scalable platform architecture. Partner with engineering stakeholders to drive adoption of shared ML infrastructure and safely integrate AI/ML-enabled solutions.

Develop and maintain AI-driven autonomous planning, scheduling, and optimization systems for Earth observation satellite operations. Build reinforcement learning algorithms, simulation and evaluation environments, and multi-objective optimization pipelines for satellite constellation tasking. Collaborate with aerospace engineers, mission operators, software engineers, and product teams to translate mission requirements into deployable systems. The role requires strong Java and Spring Boot experience, software engineering fundamentals, and machine learning or optimization expertise.

Leads full-stack software engineering and infrastructure platform development for AI-driven, self-healing data center operations. Designs Java, Python, and Node.js applications, scalable APIs, CI/CD automation, cloud-native platforms, VMware integrations, and infrastructure-as-code workflows. Develops machine learning models for forecasting, anomaly detection, and test classification, integrating them into operational systems for predictive alerts and workload orchestration. Provides technical leadership, mentors engineers, reviews code, and drives firm-wide architecture and technology decisions.

Summer analyst will build and maintain quantitative models and algorithms for systematic trading and market-making, value and hedge transactions, conduct alpha research, calibrate models, optimize portfolios, and collaborate with traders, technologists, risk, and sales to deploy data-driven trading strategies.

Develop wafer, die, and package-level models across semiconductor technology nodes. Collaborate globally to deploy computational, generative AI, agentic AI, multiphysics, and multiscale modeling solutions using sparse and high-dimensional datasets. Interpret predictive modeling insights, identify engineering findings, and communicate results to technical teams and senior leaders.

Leads the design, development, deployment, and operationalization of enterprise AI/ML, generative AI, and automation solutions. Provides technical leadership across architecture, engineering practices, evaluation, monitoring, governance, security, and responsible AI. Partners with cross-functional stakeholders, mentors engineers, establishes reusable standards, evaluates production readiness, and ensures scalable, reliable, compliant, and cost-effective delivery of AI/ML capabilities.

Build, deploy, scale, and monitor production machine learning models and infrastructure for risk management. Responsibilities include model development, data pipeline construction, cloud and distributed systems operation, Kubernetes-based deployment, CI/CD, model governance, responsible AI, testing, monitoring, and collaboration with product and data science teams.

Design, deploy, and scale responsible AI and machine learning solutions supporting healthcare provider experiences. Lead AI initiatives, provide technical direction to AI engineers, evaluate emerging technologies, and partner with product, data, and business teams. Ensure models meet enterprise standards for performance, security, governance, and scalability while contributing to cloud modernization. The role emphasizes generative AI, LLMs, conversational AI, API development, cloud hosting, and production operations.

Develop and productize machine learning and digital signal processing algorithms for speech and audio applications, including noise suppression, echo cancellation, beamforming, spatial audio, and enhancement. Research modern deep learning approaches, prototype with Python or MATLAB, and deploy optimized real-time solutions on consumer hardware. Collaborate across research, systems engineering, and product development to deliver robust audio technologies at scale.

Develop and productize advanced audio DSP and machine learning algorithms for speech and audio applications, including noise suppression, echo cancellation, beamforming, spatial audio, and enhancement. Translate research into scalable, optimized, real-time solutions for consumer devices and production hardware. Balance audio quality, ML performance, and system constraints while influencing product direction. Collaborate across algorithm research, systems engineering, deployment, and productization.

Build and evaluate applied machine learning and Generative AI solutions for product and business problems. Interns will contribute to models, agents, retrieval, experimentation, evaluation, and prototyping with mentorship from experienced engineers. Responsibilities include translating business needs into technical projects, analyzing errors and user feedback, measuring outcomes, and clearly documenting findings, trade-offs, limitations, and next steps.

Leads applied AI and machine learning initiatives, architecting scalable production systems using LLMs, AI agents, RAG, real-time predictions, and big-data processing. Provides technical direction, develops and reviews secure code, guides model and platform quality, and advises executives on ML strategy. Responsibilities include MLOps, model monitoring, data-quality improvements, production feedback loops, and building reliable solutions that improve operational efficiency and customer outcomes.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Design and build large-scale mapping algorithms and geospatial data systems for automated map reconstruction and maintenance. Develop lane, road-network, geometry, topology, and map-conflation systems; process sensor and third-party data through distributed pipelines; apply machine learning and computer vision for feature extraction and change detection; and create automated validation and quality systems. Partner with perception, localization, simulation, and platform teams while mentoring engineers and driving technical problems into production.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Develops research methods for context discovery, retrieval, multimodal representation, reasoning, tool calling, metadata management, and trustworthy AI data pipelines. Builds and evaluates foundation models across language, vision, time series, and scientific applications. The role combines applied research with hands-on engineering, including MLOps, agentic AI, knowledge-infused learning, pipeline optimization, and collaboration with software teams and external partners.

Design, train, validate, and deploy machine learning and deep learning models for e-procurement use cases including prediction, ranking, recommendations, entity resolution, document intelligence, and automation. Perform exploratory data analysis, feature engineering, experimentation, model evaluation, and error analysis. Build maintainable Python and SQL workflows, collaborate with engineering and product teams, and communicate technical findings to diverse stakeholders.

Leads the design, development, deployment, and scaling of production AI/ML solutions for healthcare. Builds cloud-based machine learning systems, data pipelines, feature workflows, and MLOps processes including CI/CD, monitoring, drift detection, retraining, and governance. Provides architecture guidance, technical leadership, mentorship, and cross-functional partnership while applying NLP, deep learning, computer vision, foundation models, and other advanced AI techniques.