Top Senior Machine Learning Jobs

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 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.

Design, deploy, evaluate, and maintain recommendation, ranking, personalization, and predictive behavior models for Taboola’s advertising platform. Build ML evaluation harnesses, automated testing frameworks, benchmarks, and guardrails. Develop data pipelines and transformations using Kafka, SQL, HDFS, Hadoop, Spark, and distributed big-data systems while collaborating with product and technical teams.

Design and own the agent execution harness for enterprise-scale conversational AI, including orchestration, multi-agent coordination, memory, reliability, observability, prompt infrastructure, evaluations, and LLM integrations. Lead architecture, technical standards, code reviews, and coaching while optimizing model routing, cost, latency, and production quality.

Design, develop, and deploy production AI and machine learning solutions for underwriting and healthcare benefits modernization. Build RAG pipelines, integrate LLMs, support agentic AI architectures using MCP and agent-to-agent protocols, and create secure high-performance APIs. Develop Python-based ML capabilities, collaborate with engineering and underwriting stakeholders, and ensure model availability, security, performance, testing, monitoring, and maintainability.

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.

Design and develop Voice AI applications with expertise in linguistics and speech. Own language and locale support, diagnose production speech failures, conduct linguistic experiments, evaluate STT/TTS systems, build analysis tooling, contribute code, collaborate with engineers and product managers, and mentor colleagues. The role requires advanced linguistics or related education, applied quantitative research experience, Voice AI development, programming skills, and knowledge of multiple languages.

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 design, development, deployment, and monitoring of production AI/ML solutions for trade surveillance and regulatory compliance. Responsibilities include developing machine learning, deep learning, and LLM systems; building MLOps pipelines; integrating AI services; implementing testing, governance, telemetry, and responsible AI practices; conducting applied research; and mentoring engineers while guiding projects from requirements through delivery.

Leads the technical direction, architecture, and roadmap for agentic machine learning systems processing global trade data. Builds ingestion, document understanding, entity resolution, inference, evaluation, observability, and feedback systems. Adapts and post-trains LLMs, ships production AI agents, mentors engineers, sets technical standards, and partners with product, engineering, data science, and customers to deliver risk, compliance, and enforcement capabilities.

Lead the technical direction and development of production machine learning and agentic systems for global trade risk, compliance, and enforcement. Responsibilities include designing data-to-decision pipelines, adapting and post-training LLMs, building evaluation and observability systems, improving model performance, mentoring engineers, and partnering with product, customers, and cross-functional teams to ship AI capabilities.

Lead architecture, optimization, deployment, and operational excellence for ServiceNow’s AI inference platform. Own model onboarding, optimized production inference across multiple backends, Kubernetes operations, intelligent gateway architecture using Envoy and ext_proc, observability, routing, incident response, and performance optimization. The role requires deep expertise in backend software engineering, distributed systems, Kubernetes, CI/CD, AI/ML, GPU compute, LLM APIs, and production operations.

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.

Leads the design, experimentation, productionization, and optimization of machine learning, deep learning, generative AI, LLM, and agentic systems. Develops Python-based proofs of concept and production solutions, evaluates and tunes models, integrates generative AI into ML platforms, improves system performance, and partners with product and engineering teams. Acts as an ML subject matter expert while mentoring practitioners and influencing product and technical decisions.

Develop full-stack web applications, Java microservices, APIs, data-streaming systems, and AI/ML solutions. Build React frontends, Kafka pipelines, MySQL schemas, Kubernetes deployments, and GitHub Actions CI/CD workflows. Maintain observability, testing, security scanning, documentation, and production reliability using enterprise monitoring tools. Participate in architectural discussions, code reviews, product lifecycle activities, and agile ceremonies while applying responsible AI practices.

Leads architecture and hands-on delivery of production-grade software, machine learning, generative AI, RAG, and AI-agent solutions. Builds cloud-native services, APIs, data integrations, MLOps/LLMOps pipelines, evaluation frameworks, and secure deployment patterns. Owns technical initiatives from experimentation through production, including reliability, observability, testing, incident resolution, responsible AI, and cost optimization. Influences cross-team architecture and roadmaps while mentoring engineers without direct management responsibilities.

Designs and deploys advanced machine learning and deep learning models, leads improvements to AI/ML systems, oversees algorithm development through integration and sustainment, analyzes complex datasets, and communicates technical insights to stakeholders. The role requires extensive experience with algorithms, MLOps pipelines, statistical analysis, experimental design, data storytelling, data governance, and data quality, plus an active Top Secret clearance and eligibility for SCI.

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.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Develops and deploys machine learning, deep learning, and visual AI models for defense and government applications. Responsibilities include data transformation, predictive modeling, anomaly detection, statistical analysis, neural network training, model reliability assessment, and AI/ML solution implementation. Leads technical projects and small teams while collaborating with stakeholders and external customers. The role requires expertise in Python-based ML frameworks, LLM prompting, Transformer architectures, and modeling and simulation tools.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Develop production machine learning systems for Glean’s Enterprise Brain, focusing on user understanding, task prediction, LLM workflows, personalization, reinforcement learning, ranking, and agentic systems. Build scalable evaluation and benchmarking loops, maintain ML pipelines and knowledge graphs, improve data and model quality, and collaborate cross-functionally to deliver reliable enterprise AI solutions.

Designs, deploys, validates, and operationalizes AI/ML solutions for infrastructure reliability, forecasting, observability, automation, and enterprise decision-making. Builds models, NLP capabilities, data pipelines, APIs, dashboards, and MLOps workflows across hybrid cloud and on-premises environments. Manages the model lifecycle, including governance, validation, monitoring, remediation, and production support. Partners with engineering, operations, risk, cybersecurity, architecture, data science, and product teams while communicating technical outcomes and risks to technical and executive stakeholders.