Top Machine Learning Jobs

Leads the design, development, validation, deployment, monitoring, and tuning of machine learning pipelines and models for IoT product performance, installation quality, outage detection, and fleet health. Builds predictive, classification, time-series, and anomaly-detection solutions using AWS, Elasticsearch, Redshift, and large-scale sensor data. Defines project scope and validation standards, collaborates cross-functionally, reports findings, supports production integrations, and mentors engineers.

PhD intern will research and develop machine learning, perception, sensor fusion, and signal-processing algorithms for indoor positioning and spatial AI. Responsibilities include experimenting with real-world deployment data, analyzing failures, improving production ML pipelines, validating models, and collaborating with engineering and product teams to ship enterprise features. The role is hands-on, product-focused, and involves research in production rather than public publication.

Develop high-performance software for silicon test pattern generation, silicon application, failure analysis, and yield learning. Create parallel graph traversal and analysis methods, collaborate across technical teams, and apply LLMs, RAG, graph-based machine learning, and reinforcement learning to electronic design automation solutions. The role also involves large-scale model training, data processing, agentic applications, and advanced multi-agent and vector database systems.

Design and productionize machine learning solutions that predict and optimize GPU-accelerated Apache Spark workloads. Develop adaptive ML systems, AI agents, and optimization tools; collaborate with customers and partners on complex deployments; track advances in ML systems; and mentor engineers in data science and machine learning. The role requires expertise in large-scale data processing, modern ML/DL methods, LLMs, generative AI, reinforcement learning, online learning, feature engineering, and boosted-tree models.

Develop and productize deep learning perception systems for autonomous driving, including traffic signal, sign, road marking, and construction object understanding. Lead end-to-end model development, data-driven improvement, simulation, augmentation, and deployment to embedded platforms. Optimize solutions for accuracy, safety, latency, robustness, and regional generalization while collaborating with data-labeling teams and contributing technical leadership.

Develop and ship a meaningful engineering project, monitor its production deployment, collaborate with a mentor and manager, test and document work, and present results to the engineering organization. The role may involve machine learning, backend or frontend development, APIs, data processing, deployment, and testing using technologies such as Python, Java, JavaScript, AWS, AngularJS, or React.

Develop and productize machine learning models for autonomous vehicle perception, including LiDAR and camera perception, sensor fusion, detection, classification, segmentation, and tracking. Build scalable data, training, metrics, and performance workflows, and implement efficient C++ code for the NVIDIA DRIVE AV platform. Collaborate across engineering teams, track advances in machine learning, and optimize models for real-time embedded inference.

Develop and productize machine learning prediction systems for autonomous driving. Responsibilities include designing end-to-end prediction solutions, researching deep learning models, supporting diverse driving environments, prioritizing data collection and labeling, using simulation and augmentation, and meeting safety, latency, and software robustness requirements. The role also involves collaboration with data teams and deployment of real-time ML solutions to embedded platforms.

Develop and optimize on-device AI capabilities for NVIDIA’s GeForce G-Assist. Evaluate Small Language Models for accuracy, robustness, conversational reliability, and contamination issues. Build multimodal and hybrid local-cloud architectures, optimize llama.cpp inference using C/C++, and integrate retrieval-augmented generation and agentic workflows involving planning and tool use. Collaborate with engineering and product teams to balance model performance, system reliability, and user experience.

Lead development of large-scale reinforcement learning frameworks and simulation pipelines for autonomous vehicle foundation models. Design closed-loop simulation, optimize reward functions and simulation quality, build scalable data-processing workflows, and improve training reliability on GPU clusters. Develop monitoring and debugging tools while collaborating with platform, product, and research teams to integrate advanced model architectures and productize machine learning solutions.

Lead the design and optimization of GPU-cluster infrastructure supporting AI and ML research. Partner with researchers, data engineers, and DevOps teams to identify bottlenecks, improve researcher efficiency, and build scalable, highly available systems. Drive infrastructure roadmaps, optimize resource utilization, define performance metrics, and oversee distributed training operations across HPC environments. The role requires deep expertise in accelerated computing, storage, scheduling, networking, containers, cloud platforms, and AI/ML frameworks.

Build and ship production AI systems for enterprise portfolio and planning workflows. Develop anomaly and signal detection, LLM-powered insight synthesis, natural-language planning rule translation, evaluation frameworks, and MCP tools. Partner with external AI providers initially, then establish internal standards for prompt design, model selection, agent architecture, reliability, and evaluation. Collaborate closely with product, design, and domain engineering teams.

Develop and deploy machine learning systems for robotic welding perception. Responsibilities include point cloud registration, seam detection, joint geometry estimation, 3D data pipelines, synthetic data generation, model evaluation, ROS integration, low-latency inference, and production deployment. Senior and staff engineers additionally lead perception research, sensor fusion, ML lifecycle strategy, distributed training, hyperparameter optimization, and technical mentorship.

Designs and delivers end-to-end AI/ML solutions on Google Cloud, including data ingestion, feature engineering, model training, deployment, and monitoring. The role leads client discovery, translates business needs into AI/ML strategies, develops roadmaps, provides technical leadership, and supports testing and user training. It requires hands-on Gemini and GCP expertise, customer-facing consulting experience, and collaboration across engineering and product teams.

Own machine learning for physical robots, including data pipeline development, dataset validation, model fine-tuning, training workflows, hardware calibration, and deployment of learned control policies. Improve data and training standards, ensure reliable performance on real robotic systems, and evaluate emerging robot-learning research. The role requires hands-on robotics and applied machine learning engineering experience, with opportunities to support government-related projects.

AI/ML engineering intern will help design, build, evaluate, and deploy LLM-powered applications and machine learning systems. Responsibilities include developing data pipelines, working with structured and unstructured data, supporting model training and optimization, preparing systems for production monitoring, and collaborating with engineers, researchers, and product teams. The role offers mentorship and hands-on experience applying AI to healthcare, government, and energy workflows.

Designs and evaluates machine learning solutions for government mission data. Responsibilities include assessing dataset quality and readiness, developing models and reproducible workflows, defining evaluation metrics, conducting error analysis, addressing bias and drift, and using AWS SageMaker for experimentation, pipelines, training, model registration, and deployment. Provides architecture and implementation recommendations while supporting batch or real-time processing decisions in secure operational environments.

Develop, train, improve, and deploy large-scale machine learning systems for fraud detection, support, user behavior prediction, and financial infrastructure. Interns will work across the full ML lifecycle, collaborate with cross-functional teams, learn unfamiliar systems, and contribute production-quality code. The role requires pursuing a relevant PhD and returning to the degree program after the internship.

Leads Hightouch’s machine learning organization, including product development, technical roadmapping, recruiting, reliability, and people management. The role focuses on personalization, product recommendations, probabilistic identity resolution, LLM-powered content generation, and scalable machine learning infrastructure for model training and inference.

Develop machine learning capabilities, custom analytics, and mission-critical AI solutions across image, video, text, geospatial, time-series, and structured data. Build and orchestrate data engineering pipelines for agentic and traditional architectures, collaborate with data scientists, software and DevOps engineers, contribute to the Chariot AI operations platform, and support customer-facing deployments.

Build production-grade machine learning and agentic AI systems for Cisco's Internet of Cognition initiative. Develop Python solutions across agent logic, systems, and infrastructure; work with LLMs and agentic frameworks; contribute to open-source codebases; and support systems through deployment and ongoing operations. Provide technical leadership, make architecture decisions, mentor engineers, and apply strong software engineering practices. Preferred experience includes emerging agent protocols, major open-source ecosystems, networking, and security.

Build core product features across Python backend and TypeScript frontend, develop a WebGPU-based rendering engine and canvas, architect AI agents for design generation and editing, and maintain evaluation and RL pipelines. Work across product, graphics, and AI, owning end-to-end technical problems and shipping high-quality user-facing product features.

Develop and deploy machine learning algorithms for defense and commercial applications, working with images, text, graph data, NLP, computer vision, reinforcement learning, and large language models. Responsibilities include analyzing large datasets, building inference models, using cloud services for training and deployment, researching emerging methods, and preparing technical documentation. The role requires Python, machine learning experience, a relevant bachelor’s degree, at least two years of experience, and an active TS/SCI clearance with CI Poly.

Leads and mentors AI/ML engineers while contributing hands-on to machine learning development. Responsibilities include building data pipelines, training and deploying models, designing scalable ML systems, conducting code and model reviews, applying LLMs and deep learning, guiding technical strategy, managing performance, hiring, and overseeing project delivery. The role requires active TS/SCI clearance and expertise in Python, machine learning frameworks, MLOps, software development, and advanced AI/ML algorithms.

Develop and implement machine learning algorithms across computer vision, natural language processing, time-series, graph data, reinforcement learning, and large language models. Analyze large multi-domain datasets, apply current research, document solutions, and support AWS-based training and deployment pipelines. Mentor team members and collaborate within interdisciplinary teams. The role requires substantial AI/ML development experience, strong Python and ML library skills, and an active TS/SCI clearance.