Top Senior Machine Learning Jobs

Develop and sustain cloud software supporting cyber intelligence and AI/ML capabilities on AWS and large-scale compute clusters. Responsibilities include requirements analysis, Java software development, cloud integration, testing, troubleshooting, RESTful services, distributed systems, big-data technologies, Linux development, and technical documentation. The role also involves LLM, RAG, agentic AI, Kubernetes, infrastructure-as-code, CI/CD, and cloud security practices. An active TS/SCI clearance with polygraph is required.

Designs, develops, tests, deploys, monitors, and continuously improves machine learning models supporting business and national security mission requirements. Integrates data pipelines, evaluates model performance, ensures data integrity, and transforms prototypes into scalable production systems. Requires expertise in machine learning frameworks, data engineering, software architecture, statistical analysis, and model validation. The role may provide technical leadership for distributed teams and requires an active TS/SCI clearance with polygraph.

Design, develop, test, deploy, and operationalize AI/ML algorithms for mission and business requirements. Build scalable production models, integrate data pipelines, evaluate model performance, and manage training, retraining, monitoring, and continuous improvement. Apply statistical analysis, data governance, software architecture, and data modeling principles. Support system integration and verification/validation activities, with potential leadership of distributed teams and technical customer engagement.

Develop and deploy machine learning systems for search, ranking, recommendations, pricing, fraud detection, demand prediction, and personalization. Own the full ML lifecycle, including data sourcing, feature engineering, model training, real-time inference, batch workflows, deployment, and monitoring. Collaborate with product, design, and operations while leveraging generative AI and cloud-native infrastructure to deliver scalable product features.

The Senior Analyst will develop AI and ML algorithms, optimize data systems, and present research findings. Requires extensive experience in model development, system architecture, and MLOps.

Build and operate scalable ML inference infrastructure powering Claude’s safety systems. Responsibilities include productionizing safety research, optimizing real-time inference latency and throughput, developing monitoring and observability tools, implementing testing and deployment automation, and supporting reliable classifier rollouts. The role requires collaboration with researchers, inference engineers, security teams, and alignment teams to deliver high-throughput, low-latency, safety-critical ML systems.

Build scalable data and ML systems for LLM training, post-training, evaluation, and continuous improvement. Responsibilities include creating data pipelines, human-in-the-loop labeling workflows, synthetic datasets, quality metrics, and automated evaluation systems. The role applies LLMs and machine learning to data generation and validation, processes large-scale structured and unstructured data, translates research into production systems, and provides technical leadership and mentorship.

Develops AI/ML prototypes, APIs, microservices, integrations, and lightweight user interfaces for federal and DoD-aligned mission workflows. The role integrates and optimizes machine learning models for dashboards and applications, supports data ingestion and preprocessing, validates performance, and helps transition models into production. Collaboration with data scientists, architects, developers, PMO leadership, and cybersecurity teams is required. The position requires five years of relevant experience, a bachelor’s degree, ITIL v4 Foundation certification, and Public Trust or active Secret clearance.

Develop and productionize machine learning and hybrid physics-ML methods for seismic imaging, inversion, and velocity model building. The role applies deep learning, generative models, neural operators, and physics-informed approaches to large seismic datasets using GPU and cloud infrastructure. Responsibilities include validating methods against physics-based approaches, quantifying uncertainty, documenting reproducible workflows, and communicating results to geophysicists, data scientists, and asset teams.

Design and implement ML-based prototypes, tools, and systems for AI security. Collaborate with researchers to execute experiments, analyze large cybersecurity datasets, support adversarial ML and red teaming, and translate research into deployable, maintainable software solutions.

Develops and deploys AI and machine learning solutions for intelligence and cyber workflows in classified environments. Responsibilities include building LLM-powered workflows and agent-based automation, integrating AI into operational systems, developing data pipelines, evaluating models, optimizing workflows, and conducting operational testing. The role collaborates with mission users, analysts, software developers, and leadership to deliver reliable, scalable, secure, and production-ready AI capabilities.

Leads the architecture, development, deployment, and optimization of enterprise machine learning solutions. Responsibilities include designing scalable ML systems, developing predictive models, supporting MLOps and model lifecycle management, improving data quality and feature engineering, implementing governance and validation frameworks, and mentoring technical teams. The role also contributes to AI strategy, innovation initiatives, responsible AI practices, and organizational modernization.

Design and scale real-time bidding systems, auction mechanisms, bid forecasting, pacing algorithms, and marketplace intelligence models. Apply machine learning, reinforcement learning, control theory, experimentation, and large-scale data analysis to optimize advertiser returns. Lead projects from architecture through offline evaluation and online A/B testing, collaborating with product, infrastructure, and engineering teams to deliver measurable business impact.

Design and build a next-generation analytics engine and cost-based query optimizer delivering major performance improvements over cloud data warehouses. The founding team member will develop data-learning optimization technologies, ship cloud data products through multiple iterations, and work across relational databases, NoSQL, data lakes, and OLAP systems. Responsibilities include rapid experimentation, debugging, collaboration, and product development in a startup environment.

Build, deploy, improve, and maintain production machine learning models and systems. Design offline and online experiments, analyze results, and identify actionable insights. Collaborate with ML Infrastructure and cross-functional stakeholders to develop technical platforms and deliver strategic ML product initiatives. Mentor engineers and interns, and help recruit diverse machine learning talent.

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.

Develop and deploy machine learning models for wafer fabrication yield improvement, defect screening, and process optimization. Integrate AI/ML pipelines with manufacturing and MES systems using edge, cloud, API, and container technologies. Build data pipelines, dashboards, monitoring tools, and analytical methods while supporting experiments and continuous improvement. Collaborate with photonics, manufacturing, reliability, and software engineers, establish ML best practices, and communicate results across sites.

Develop and productionize online mapping machine learning models and algorithms for autonomous vehicles. Analyze system performance, improve data, architectures, and evaluation, build training pipelines, and integrate models with centralized autonomy systems. Collaborate with autonomy, infrastructure, and systems teams to deliver robust mapping solutions using camera and LiDAR data in uncertain environments.

Lead and deliver high-impact ML solutions for telecommunications: set vision and roadmaps, implement and oversee research-grade models, mentor data scientists, align cross-functional teams, improve data foundations, and scale robust MLOps-driven products.

Build, ship, and own end-to-end ML models for vendor matching, order routing, and supply-chain optimization. Partner with data engineering to productionize models, quantify impact, document work, and implement decision logic components toward agentic AI in a fast-moving healthcare startup environment.

Design and lead large-scale machine learning and backend systems for advertiser growth, including budget pacing algorithms, GenAI creative generation infrastructure, marketplace experimentation, high-throughput billing pipelines, and real-time stream processing. Analyze marketplace and financial data, optimize advertiser performance, ensure billing accuracy and fault tolerance, lead multi-quarter technical roadmaps, and mentor senior engineers.

Drive applied research to enable continuous learning for agents: design and evaluate LLM post-training methods (fine-tuning, RL, distillation), run large-scale GPU experiments, validate approaches on customer tasks, and help deploy and scale models and infrastructure across the stack.

Build core infrastructure and ML models for a cloud-native IDE: architect the development environment, integrate with GitHub and Linear, fine-tune models, develop always-on AI agents, and design systems that continuously learn from user behavior.

Design and build full-stack features for an LLM-powered coaching platform: architect backend services and APIs, integrate models, craft responsive front-end experiences, ensure enterprise-scale performance and security, drive CI/CD and observability, debug across the stack, and mentor engineers while collaborating with product and design.

Build and maintain production AI/ML models and pipelines for an NDR platform; develop scalable, secure backend services; optimize detection algorithms and reduce false positives; integrate ML features into product architecture; mentor engineers; participate in code reviews, technical strategy, and documentation; operate cloud-native, containerized services and manage infrastructure as code.