Top Machine Learning Jobs

Designs and implements AI/ML capabilities for cyber and intelligence workflows, including LLM-powered systems, agentic automation, API and data-store integrations, and production deployments in classified environments. Works with mission users to document workflows, translate decision points into executable logic, and improve analytical efficiency. Collaborates with operators, analysts, developers, and leadership while ensuring secure, governed, reliable system integration.

Lead the design, development, and deployment of scalable machine learning and generative AI solutions. Partner with stakeholders, data scientists, and software engineers to build AI platforms, tooling, agents, and autonomous workflows. Analyze large datasets, optimize and fine-tune models, apply emerging AI technologies, and establish reusable capabilities and technical standards across the organization.

Develop, deploy, and optimize scalable machine learning solutions, including predictive models, deep learning systems, data pipelines, and model-serving infrastructure. Monitor model performance, manage retraining and lifecycle processes, ensure quality and compliance, and support responsible AI practices. Collaborate with data scientists, engineers, product teams, and business stakeholders while researching emerging AI technologies and improving AI architectures.

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.

Leads AI/ML engineering strategy, architecture, training, and implementation for an AI test laboratory and intern mentorship program. Builds production-grade data pipelines, MLOps workflows, model training and deployment systems, monitoring, explainability, and drift detection. Collaborates across technical, security, and stakeholder teams to deliver scalable AI capabilities, develop curricula, and ensure compliance with government requirements. The role also provides expert briefings, mentors junior personnel, and supports AI modernization across national security environments.

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.

Design, implement, and scale end-to-end retrieval-augmented generation (RAG) pipelines. Build and optimize retrieval systems, embeddings, vector search, and LLM orchestration. Collaborate with product, design, and MLOps to productionize robust ML systems across large proprietary datasets.

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.

Develop machine learning solutions that improve wireless communication systems using RF signal processing, software-defined radio data, probability, statistics, and AI. Build scalable, high-throughput systems for edge devices and data centers, collaborate with designers, infrastructure engineers, and research scientists, integrate ML algorithms into prototypes and products, and apply current research to wireless and embedded systems.

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.

Lead the design and operation of analytical and machine-learning systems, define AI/ML strategy, and ensure model reliability and compliance in production.

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 deploy machine learning models for creating HD maps, utilizing advanced ML techniques and collaborating with other teams to deliver reliable solutions.

As a Machine Learning Systems Engineer, you will optimize distributed training systems, enhance performance profiling, and develop high-performance GPUs for machine learning workloads.

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.

Role involves assisting with machine learning model improvement through tasks like data labeling, content evaluation, and participation in testing activities.

Design, prototype, and productionize AI systems and infrastructure—recommendations, natural language interfaces, agentic workflows—partnering with product and engineering to scale ML across the full lifecycle (data curation, training, deployment, monitoring).

Design, prototype, and productionize AI/ML systems (recommendations, NL interfaces, agentic workflows). Build and scale ML infrastructure, partner with product/design/engineering, and solve UX problems at the intersection of AI and BI.

Design, prototype, and productionize AI systems (recommendations, natural language interfaces, agents). Partner with product and engineering to identify AI opportunities, build scalable ML infrastructure, and manage the full ML lifecycle from data curation through training, deployment, and monitoring.