Top Senior Machine Learning Jobs

Leads technical vision for ads conversion machine learning, developing low-latency deep neural networks and modeling user intent from text, visual, and behavioral signals. The role includes mentoring engineers, guiding applied ML projects, collaborating on ad products, using AI tools to accelerate development, and applying statistical methods and A/B testing to improve model quality and iteration speed.

Develop and deploy machine learning capabilities for radar systems, including labeling automation, active learning, classification, tracking, data augmentation, and self-supervised models. Build training and evaluation pipelines, assess robustness across rare targets and field conditions, analyze failures, and integrate models into production radar products. Optimize accuracy, latency, computation, and reliability for embedded, resource-constrained systems.

Lead the technical strategy and development of machine learning solutions for Uber’s homepage personalization, including sequential recommendation, intent-aware ranking, LLM-based systems, and agentic AI. Partner with product teams to improve engagement and business outcomes, influence technical direction across teams, and mentor machine learning engineers.

Design, optimize, and deploy machine learning models for resource-constrained edge devices, including mobile and embedded platforms. Responsibilities include model compression, quantization, pruning, hardware-aware optimization, performance profiling, production deployment, and systems engineering. The role also addresses on-device privacy and security, collaborates cross-functionally, and may involve NPU/DSP toolchains, federated learning, personalization, safety-critical deployments, and on-device LLM optimization.

Own applied machine learning and LLM systems end to end, translating ambiguous business problems into datasets, models, evaluation frameworks, production pipelines, and deployed services. Build Python-based data and modeling infrastructure, improve LLM extraction and research agents, manage observability and reliability, and communicate technical tradeoffs with stakeholders. The role requires autonomous execution in a lean, onsite New York City engineering environment.

Develop and deploy scalable AI and machine learning solutions for client use cases. Responsibilities include designing machine learning models, extracting insights from complex data, deploying production systems, creating agentic and multi-agent architectures, building conversational AI applications, and integrating cloud AI services. The role also involves stakeholder collaboration, system specification, testing, issue management, and supporting enterprise GenAI delivery.

Leads 30+ engineers in designing and delivering scalable AdTech measurement and reporting platforms. Drives technical strategy, architecture, design reviews, production issue resolution, and cross-functional execution. Develops distributed, cloud-native systems using machine learning, statistical methods, real-time streaming, transformer models, and GenAI. Oversees solutions for attribution, incrementality, data cleanrooms, partner integrations, and AI-powered reporting supporting Walmart’s digital advertising business.

Build, train, evaluate, and deploy production machine learning and LLM systems for legal document intelligence. Develop NLP solutions for information extraction, summarization, search, AI agents, and document analysis. Create scalable ML pipelines, evaluation frameworks, and reliability metrics while optimizing model performance. Collaborate with engineering, product, legal, data, and security teams to deliver secure AI capabilities and translate business problems into production solutions.

Designs, develops, and productionizes machine learning, deep learning, generative AI, LLM, simulation, and optimization solutions. Responsibilities include building and scaling model-serving pipelines, improving accuracy and performance, integrating models into production, developing project architectures, defining ETL requirements, and collaborating with data scientists, engineers, analysts, and stakeholders. The role requires office attendance five days per week in Arlington, Virginia.

Build and scale end-to-end machine learning infrastructure for computer vision and multimodal AI systems. Responsibilities include developing training and deployment pipelines, managing GPU cloud infrastructure, optimizing low-latency model serving, and establishing monitoring and evaluation frameworks for reliable production performance. The role partners with research, computer vision, and product teams to operationalize models across warehouse environments.

Leads the technical direction and execution of a machine learning team focused on personalization, relevance, ranking, search, and advertising. Designs, scales, and operates production ML systems, including training, evaluation, deployment, and serving infrastructure. Partners with product, data, and engineering stakeholders, drives architecture and system design decisions, mentors engineers, and uses AI tools to accelerate development and explore LLM-powered features.

Designs, optimizes, and deploys machine learning models for resource-constrained edge devices, including mobile and embedded platforms. Responsibilities include model compression, quantization, pruning, hardware-aware optimization, performance profiling, production deployment, and addressing privacy and security considerations. The role collaborates cross-functionally to deliver reliable on-device AI and may involve NPU/DSP toolchains, federated learning, safety-critical deployments, and on-device LLM optimization.

Designs and deploys enterprise AI/ML solutions, scalable models, data pipelines, and MLOps architectures in cloud production environments. Responsibilities include model monitoring and optimization, responsible AI implementation, workflow automation, emerging technology evaluation, cross-functional architecture collaboration, code reviews, and mentoring. The role focuses on healthcare challenges and supports production-grade machine learning systems, including real-time inference and continuous integration and deployment.

Own and evolve Braze’s ML platform for globally scaled training pipelines, model serving, deployment, orchestration, and observability. Lead complex infrastructure initiatives, improve reliability and cost efficiency, manage incident response, and establish production quality standards. Partner across teams, shape technical strategy, mentor senior engineers and data scientists, and connect platform decisions to customer and business outcomes.

Design, build, deploy, and optimize large-scale machine learning models and infrastructure for Uber Maps, including high-throughput, low-latency prediction systems and APIs. Develop distributed ML pipelines, improve routing and ETA applications, explore optimization solutions, and provide technical leadership. Collaborate with product managers, scientists, designers, and engineers to solve complex, real-world mobility problems.

Designs, develops, tests, operates, and maintains production software with full-stack ownership. Builds scalable, reliable, high-performing applications and complex APIs, with emphasis on backend and machine learning systems, GenAI, ML platforms, and embeddings. Conducts code reviews, mentors engineers, analyzes failures, improves reliability, and collaborates with Product, Design, and partner engineering teams using agile and DevOps practices.

Designs, trains, evaluates, and operationalizes intelligent document-processing solutions using OCR, machine learning, LLMs, Python, and cloud data pipelines. Builds extraction, validation, reconciliation, exception-handling, and human-review workflows for sensitive information. Develops secure, auditable, configuration-driven systems with API integrations, monitoring, testing, CI/CD, and infrastructure as code. Partners with business experts to define document fields, quality thresholds, validation rules, and acceptance criteria.

Lead Adyen’s Personalize Intelligence team developing machine-learning systems for real-time Checkout optimization and AI products. Provide technical leadership on architecture, reliability, and scalable services; coach machine learning scientists and data engineers; define goals and product roadmaps with Product; collaborate across Design, Engineering, Data, and leadership; monitor system performance; foster an inclusive, high-performing culture; and recruit new team members.

Lead development of auction and bidding algorithms for Microsoft Advertising: design and implement bidding strategies and auction mechanisms, run large-scale experiments, build automation and marketplace optimization systems, and develop causal and ML models to improve advertiser ROI and long-term marketplace health.

Lead applied AI and machine learning research for Pinterest’s generative recommendation and reinforcement learning systems. Architect, build, and deploy novel ML solutions at scale, collaborate across product, engineering, and data science, identify research opportunities, shape long-term strategy, mentor others, and translate advanced research involving recommendation systems, LLMs, and VLMs into production impact.

Build and optimize machine learning models, ranking systems, retrieval systems, and personalized Search experiences for Spotify. Develop production-ready solutions from experimentation through deployment, explore conversational AI and agentic approaches, evaluate models through experimentation and performance metrics, and contribute to scalable machine learning infrastructure. Collaborate with engineering, product, design, and research teams to improve discovery for millions of listeners.

Build and operate production trust and safety systems that detect and disrupt platform abuse at internet scale. Responsibilities include developing ML infrastructure, training and feature pipelines, serving systems, evaluation frameworks, and LLM/classical ML detection solutions. Own systems from model integration through deployment, monitoring, and iteration while collaborating with security, product, and infrastructure teams.

Design, optimize, and deploy machine learning models for mobile, embedded, and specialized edge hardware. Responsibilities include quantization, pruning, distillation, accelerator optimization, inference runtime development, performance benchmarking, hybrid edge-cloud architecture, secure model deployment, telemetry, privacy, and responsible AI. The engineer will collaborate with hardware, firmware, and product teams while maintaining technical documentation and evaluating emerging edge AI technologies.

Develop and scale machine learning recommendation systems for Xumo streaming products. Responsibilities include feature engineering, big-data analysis, model training and deployment, offline evaluation, online A/B testing, data pipeline management, model serving optimization, technical documentation, proofs of concept, and cross-functional collaboration. The role also mentors junior engineers and contributes to patents and APIs.

Design, productionize, and operate LLM-powered agents on NEO across CIB and Payments. Build production-grade Graph RAG, agent memory, entitlement-aware context, multi-agent workflows, tool integrations, evaluations, guardrails, and observability. Deploy solutions on AWS or Azure while meeting security, resiliency, compliance, and SDLC standards. Partner with technical, product, and business stakeholders to deliver supported AI agent products.