Top Machine Learning Engineer Jobs

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.

Lead AI/ML initiatives for Air Force condition-based maintenance, from predictive model development through production deployment. Architect data pipelines, develop machine learning, deep learning, NLP, and generative AI solutions, and build knowledge graph capabilities. Provide technical leadership, integrate models into applications and workflows, and monitor data quality, model performance, drift, and bias. Collaborate with engineering, cloud, software, and product teams while supporting defense aviation logistics and sustainment missions.

Design, deploy, and maintain GenAI applications, RAG pipelines, machine learning models, and data pipelines using Python, SQL, AWS, and Snowflake. Build AI-enabled REST APIs and microservices, evaluate LLM performance, create dashboards, improve data quality, and implement CI/CD and automated testing. Document architectures and experiments while supporting reliable production releases in a regulated environment.

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.

Lead the architecture and implementation of enterprise AI platforms, autonomous agent frameworks, ML infrastructure, and multi-tenant orchestration systems. Build scalable REST services, event-driven pipelines, MLOps capabilities, model monitoring, and secure low-latency inference systems using cloud-native technologies. Partner with engineering, product, data, SRE, and compliance teams while providing technical leadership for reliable AI systems serving millions of users and enterprise tenants.

Research, prototype, implement, and evaluate predictive machine learning products using large financial datasets. Responsibilities include statistical analysis, hypothesis testing, anomaly detection, production model development, testing, peer review, and maintaining expertise in machine learning and data science tools. The role also involves publishing machine learning research, writing production code, and developing agentic systems using large language models.

Develops predictive, data-driven products using statistical analysis and machine learning. Responsibilities include researching large financial datasets, identifying patterns and anomalies, designing and evaluating models, writing production code and tests, conducting peer reviews, publishing machine learning research, and applying modern AI, agentic systems, and LLM technologies.

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.

Build and operate production AI/ML systems at scale. Responsibilities include monitoring model performance, detecting drift, implementing evaluation loops and safety guardrails, managing fallbacks, developing automated retraining and deployment pipelines, resolving live incidents, and collaborating on cloud, on-premises, and edge architectures. The role also involves code reviews, mentoring, and promoting secure and ethical AI practices.

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.

Fine-tune, optimize, benchmark, and deploy Small Language Models, especially Alibaba Qwen, for production inference. Improve accuracy, latency, throughput, GPU efficiency, memory usage, and model serving with vLLM or SGLang. Develop low-latency real-time audio intelligence pipelines, optimize KV caching and quantization, and collaborate with engineering and data teams to deliver scalable, high-performance ML systems.

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.

Master’s machine learning engineering intern developing, training, improving, and deploying large-scale production ML systems. Projects may support fraud detection, user behavior prediction, foundation models, and enhanced customer support. The role involves end-to-end model development, collaboration across teams, learning unfamiliar systems, and communicating progress effectively.

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.

Develop and integrate AI/ML capabilities for classified Battlespace C2, threat intelligence, data extraction, modeling, dashboards, and threat inject generation. Configure virtualized test environments, execute cybersecurity scans, maintain secure baselines, support RMF and ATO compliance, and perform integration and regression testing. Create technical documentation, track defects and configuration changes, and collaborate with software, systems, cybersecurity, and government stakeholders. The role requires Secret clearance with Top Secret/SCI eligibility and 5% travel.

Design, train, evaluate, and deploy reinforcement learning systems for complex decision-making problems. Responsibilities include developing reward functions, working with simulation environments, collecting experience at scale, training neural network policies on GPU clusters, and moving RL solutions into production. The role requires strong theoretical knowledge, engineering judgment, and communication skills, with opportunities involving RLHF, multi-agent or hierarchical RL, robotics, control systems, and autonomous driving.

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 systems engineering. The role also addresses on-device privacy, security, and cross-functional collaboration, with preferred experience in NPU/DSP toolchains, federated learning, safety-critical deployments, open-source frameworks, and on-device LLM optimization.

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, deploy, and maintain machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Use AI tools to develop scalable services while ensuring code correctness, security, performance, and architectural quality. Mentor collaborators and solve ambiguous, large-scale problems.

Build and deploy machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Develop scalable, secure, production-ready services using AI-assisted engineering workflows, while contributing to ranking, recommendations, search, content understanding, or image-generation applications.

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.

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.

Lead researchers and developers in creating computer vision AI/ML solutions for remote sensing, including automatic target recognition and multimodal EO/SAR data fusion. Analyze datasets, research supervised and unsupervised approaches, train and optimize deep learning models, deploy them to cloud environments, communicate technical findings, and produce documentation. The role requires technical leadership, customer collaboration, and an active TS/SCI clearance.

Develop and implement AI/ML algorithms across computer vision, NLP, time series, reinforcement learning, and large language models. Analyze multimodal datasets, apply research findings, document systems, support AWS-based training and deployment pipelines, and mentor team members. The role requires substantial AI/ML development experience, advanced Python and machine learning library proficiency, deep learning expertise, and an active TS/SCI clearance.

Develop and implement AI/ML algorithms for defense and commercial applications, analyzing image, text, and graph datasets to build actionable models. Use cloud services to train and deploy models, apply statistical and machine learning techniques, review research, and create technical documentation. The role involves collaboration with software developers and researchers and requires an active TS/SCI clearance with CI Poly.