Top Machine Learning Jobs

Develop and evaluate AI/ML solutions for R&D projects supporting DoD systems. Responsibilities include model development, independent verification and validation, effectiveness reporting, vendor and research-partner collaboration, code review, and integration with external development teams. The role emphasizes mathematical and data science foundations, model validation, secure deployment, and constructive technical assessment across machine learning, LLMs, reinforcement learning, diffusion models, explainable AI, and adversarial AI.

Ten-week machine learning engineering internship focused on building systems for training and deploying large-scale ML models. Interns collaborate with researchers, hardware experts, and software engineers on GPU acceleration, distributed computing, and open-source tools supporting trading strategies. The program includes technical training, mentorship, professional development, financial markets education, and team activities. Successful interns may receive an opportunity for a graduate position.

Builds scalable data pipelines and AI/ML infrastructure, prepares datasets, develops features, supports model training, validates models, and deploys secure cloud-based solutions. Work includes predictive maintenance, NLP, anomaly detection, test generation, MLOps, data provenance, explainability, documentation, and monitoring. The role requires an active Secret Clearance and supports projects at Maxwell-Gunter Annex in Alabama.

Build and operate production trust and safety systems that detect and disrupt platform abuse at internet scale. Responsibilities include developing ML infrastructure, training and feature pipelines, serving systems, evaluation frameworks, and LLM/classical ML detection solutions. Own systems from model integration through deployment, monitoring, and iteration while collaborating with security, product, and infrastructure teams.

Lead the architecture, research, evaluation, and production deployment of agentic AI and LLM systems for healthcare applications. Build multi-agent orchestration, memory, tool-use, fine-tuning, prompt optimization, evaluation, and data curation pipelines. Collaborate with clinical, product, infrastructure, and software teams to deliver secure, low-latency systems, while mentoring engineers and establishing technical standards.

Lead the design and operation of scalable machine learning infrastructure, including data generation, distributed training, production serving, evaluation consistency, accelerator optimization, reliability, and observability. Set technical direction, guide architecture, mentor engineers, and collaborate across modeling, evaluation, and platform teams. Build systems supporting autonomous driving models, simulation data, and large-scale production inference.

Build and maintain data models, ETL pipelines, and production machine learning systems supporting recommendations, personalization, pricing, search, and marketplace optimization. Own features end to end, conduct statistically rigorous experimentation, monitor deployments, write technical designs, and collaborate with engineering, data science, and product teams to deliver measurable business impact.

Develops, integrates, tests, and deploys autonomy, artificial intelligence, and machine learning algorithms for mission systems. Responsibilities include perception, motion and mission planning, controls, software design, requirements translation, model implementation, troubleshooting, debugging, human-machine interface development, and test evaluation across the technology development lifecycle.

Develops, integrates, tests, and deploys autonomy, artificial intelligence, and machine learning algorithms for mission systems. Responsibilities include translating requirements into software, implementing perception, planning, controls, and human-machine interfaces, training models, troubleshooting defects, evaluating test results, and supporting the full technology development lifecycle.

Designs, deploys, and supports production machine learning solutions, including CI/CD pipelines, APIs, batch and real-time inference, model monitoring, data validation, and MLOps tooling. Partners with data scientists, actuaries, engineers, and business stakeholders to transition models into reliable applications. Responsibilities include troubleshooting, governance, testing, performance optimization, documentation, and providing technical guidance on production machine learning practices.

Design, develop, and productionize machine learning models and pricing algorithms for Uber’s large-scale, real-time marketplace systems. The role focuses on deep learning, causal modeling, reinforcement learning, optimization, and online serving at high volume. Responsibilities include collaborating with engineers, product managers, and scientists, shaping technical direction, solving complex marketplace problems, mentoring junior engineers, and monitoring ML solutions across offline and online environments.

Design, productionize, and operate LLM-powered agents on NEO across CIB and Payments. Build production-grade Graph RAG, agent memory, entitlement-aware context, multi-agent workflows, tool integrations, evaluations, guardrails, and observability. Deploy solutions on AWS or Azure while meeting security, resiliency, compliance, and SDLC standards. Partner with technical, product, and business stakeholders to deliver supported AI agent products.

Leads the end-to-end development and productionization of AI/ML solutions for trade surveillance and regulatory compliance. Responsibilities include model development, LLM and generative AI experimentation, MLOps pipelines, data engineering collaboration, API integration, monitoring, responsible AI governance, and technical mentorship. The role guides projects from requirements through implementation and delivery while partnering with data science and software engineering teams.

Leads and scales the engineering organization responsible for satellite imagery analytics, machine learning models, and data pipelines. Oversees engineering managers and distributed teams, staffing, delivery, operating models, classified and unclassified environments, foundation-model adoption, cost management, and cross-team commitments. Requires production experience with machine learning, computer vision, or geospatial analytics, plus experience managing engineering leaders and working with export-controlled and government-cleared environments.

Leads AI/ML research and development, owning enterprise evaluation methodology, predictive and decision models, experimentation, causal inference, model economics, and production deployment. Builds evaluation suites and models hands-on, monitors performance and cost, prototypes agentic techniques, and guides AI-native engineering practices. The role also develops a small team, raises organizational ML fluency, supports data-as-a-service benchmarks, and communicates model uncertainty and business value to executives.

Leads development, integration, testing, validation, and deployment of AI and machine-learning capabilities for radar, communications, electronic warfare, and other signal-processing applications. Owns technical workstreams, develops models, integrates capabilities into mission-critical systems, collaborates across engineering and customer teams, and supports transition to fielded mission systems.

Lead and manage the ML Dataset Platform team while defining its technical direction, roadmap, APIs, tooling, storage formats, versioning, lineage, and data-loading infrastructure. Build scalable systems for petabyte-scale multimodal datasets, optimize training data access across clouds, partner with ML platform and model teams, hire and mentor engineers, and drive platform adoption across the organization.

Lead the architecture and evolution of DraftKings’ machine learning platform, including real-time data pipelines, lakehouse governance, distributed data systems, and scalable model-serving infrastructure. Establish engineering standards, guide complex initiatives, improve reliability and cloud cost efficiency, and partner across engineering and data teams. The role also includes mentoring engineers, managing production operations, and developing AI-assisted automation tools.

Build, fine-tune, evaluate, and deploy computer vision and multimodal machine learning models in production. Develop scalable data pipelines, evaluation frameworks, real-time processing systems, and cloud infrastructure with monitoring and automated scaling. Improve image processing, barcode decoding, and OCR capabilities while partnering with engineering and fulfillment teams. Contribute through code reviews, documentation, testing, technical leadership, and mentorship.

Lead and mentor an engineering team developing production-grade deep learning and computer vision models for autonomous driving. Oversee roadmap execution, model deployment, multi-sensor fusion, spatio-temporal modeling, software quality, validation, and collaboration with motion planning, localization, infrastructure, and hardware teams. Guide models from research prototypes through real-time vehicle deployment while ensuring latency, reliability, and safety-critical performance.

Design, train, evaluate, and optimize machine learning models for wireless network use cases and real-time edge inference. Build telemetry data pipelines, deploy containerized models on Kubernetes and GPU infrastructure, develop AI-driven network applications, and implement MLOps practices. Benchmark resource contention, ensure model security and provenance, document results, and collaborate with wireless, RF, cybersecurity, and government stakeholders. The role requires hands-on lab work, technical communication, and eligibility for a Secret clearance.

Lead production machine learning and AI engineering initiatives from data assessment through deployment, evaluation, monitoring, and continuous improvement. Build reliable LLM workflows, data and feature pipelines, model-serving systems, and MLOps infrastructure across forecasting, recommendations, search, marketing, artist intelligence, and ecosystem integrity. Partner with technical and business stakeholders, establish evaluation and observability practices, mentor engineers, and deliver scalable, cost-effective decision-support capabilities.

Develop, evaluate, deploy, and monitor machine learning models for government and national security applications. Convert prototypes into production-ready Python services, integrate models with APIs and data workflows, and support MLOps practices including experiment tracking, model versioning, validation, deployment, monitoring, and rollback. Build automated tests, troubleshoot performance issues, document implementations, and collaborate with data scientists, data engineers, software developers, and government stakeholders in an Agile environment.

Develop and productionize machine learning classifiers and evaluation systems for assessing model behavior. Responsibilities include supervised fine-tuning, distillation, rigorous evaluation, inference optimization, deployment, observability, reliability, and ongoing improvement. The role partners with researchers and software engineers to translate open-ended research needs into measurable ML objectives and scalable production systems, balancing model quality, latency, compute utilization, and cost.

Develop embedded machine learning and robotics solutions, including control, computer vision, sensor fusion, detection, and regression algorithms. Responsibilities include writing production Python and C++ code, implementing ROS-based robotic systems, building ML training and deployment pipelines, creating efficient inter-process communication libraries, and optimizing ML software for embedded and hardware-accelerated devices.