Top Machine Learning Jobs

Develop and deploy production machine learning systems for national security applications. Build Azure-based data and ML pipelines, anomaly detection and predictive models, agentic AI and RAG solutions, and cloud-native processing workflows. Support Docker and Kubernetes deployments, CI/CD, infrastructure as code, monitoring, troubleshooting, and optimization. Evaluate emerging AI technologies, communicate technical recommendations, and collaborate with government clients and cross-functional teams.

Designs, develops, tests, validates, and deploys AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Builds data and model-training pipelines, leverages cloud platforms, evaluates model performance, documents methodologies, collaborates with technical teams, and guides prototype-to-production transitions. Requires five years of relevant experience and an active TS/SCI clearance with polygraph.

Develop, train, and evaluate machine learning and deep learning algorithms for audio applications. Research audio ML and digital signal processing methods, prototype algorithms for software and hardware platforms, curate datasets, and collaborate with interdisciplinary researchers and engineers. The role includes evaluating audio technologies, presenting findings, and potentially contributing to Bose products, patents, and publications.

Research and develop machine learning and deep learning algorithms for audio applications, including speech enhancement, spatial audio, voice pickup, and hearing augmentation. The intern will build datasets, prototype and integrate models into software or hardware, evaluate audio algorithms, collaborate with interdisciplinary teams, and present findings. Work may contribute to Bose products, patents, and research publications.

Lead development of auction and bidding algorithms for Microsoft Advertising: design and implement bidding strategies and auction mechanisms, run large-scale experiments, build automation and marketplace optimization systems, and develop causal and ML models to improve advertiser ROI and long-term marketplace health.

Leads Posh’s unified data and ML organization of eight across data science, machine learning, and analytics/data engineering. Owns discovery, ranking, recommendations, search, experimentation, causal inference, data infrastructure, AI-native analytics, and technical roadmap development. This player-coach role requires mentoring senior and mid-level talent, conducting hands-on model and experiment reviews, improving ML foundations, and translating product goals into measurable technical investments.

Lead development, integration, testing, validation, and deployment of AI and machine-learning capabilities for radar, communications, and electronic warfare signal-processing applications. Own technical workstreams, develop mission-system algorithms, integrate models into complex software platforms, troubleshoot deployments, and collaborate with engineering, program, and government customer teams. The role requires expertise in signal processing, ML model development, evaluation, and deployment, with potential work involving multiple sensing modalities and mission-critical defense systems.

Leads the design, development, deployment, and operation of machine learning models, platforms, data pipelines, and production services at scale. Responsibilities include model training, monitoring, optimization, cloud infrastructure, distributed systems, CI/CD, responsible AI, and collaboration with product and data science teams. The role also focuses on RAG pipelines, agentic AI systems, LLM orchestration, and resilient production deployments.

Build and deploy production-grade AI/ML software, APIs, and applications for healthcare provider use cases. Develop prompt-based AI capabilities, integrate enterprise data and cloud services, create automated tests and evaluation pipelines, and ensure security, observability, scalability, and reliability. Collaborate with product, architecture, data science, and platform teams while owning technical work from proof of concept through production.

Lead the design, development, deployment, and optimization of advanced machine learning, LLM, and agentic AI systems. Responsibilities include analyzing large datasets, integrating models into products, monitoring production performance, and collaborating with technical and business teams. The role requires architectural leadership and hands-on engineering across deep learning, recommendation systems, personalization, RAG, agent orchestration, model evaluation, guardrails, and inference optimization.

Own machine learning problems end to end, from framing ambiguous requirements through production deployment. Build NLP and LLM systems, data pipelines, model-serving infrastructure, evaluation and monitoring systems. Select data, models, metrics and system designs while critically reviewing AI coding agents. The role combines research and engineering, requiring strong ML fundamentals, expert Python, testing discipline and independent ownership of complex problems.

Develops, integrates, tests, and deploys AI, machine learning, and autonomy algorithms for mission systems. Responsibilities include requirements analysis, software design, perception, planning, controls, human-machine interfaces, systems modeling, troubleshooting, debugging, and field testing. The role supports the full technology development lifecycle and requires familiarity with tactical networks, ATAK, TAK Server, and current AI and autonomy research.

Leads AI-driven engineering automation across the defense acquisition lifecycle. Owns the product roadmap, software architecture, data ontology, and AI development effort. Researches and develops machine learning, knowledge representation, and deep learning models for engineering and sustainment work products. Collaborates across software, simulation, UX, testing, and external teams to deliver validated prototypes and operational systems. Applies expertise in defense logistics, sustainment, supply chain, and aerospace product support while ensuring performance, security, and compliance.

Build and evaluate machine learning systems for an interactive consumer education product. Own AI features end to end, including goal setting, evaluation tooling, production deployment, orchestration, user modeling, personalization, feedback loops, experimentation, and quality, safety, latency, and engagement measurement. Partner with learning specialists and product engineers to deliver high-volume, low-latency language-model functionality.

Develops, integrates, tests, and deploys autonomy, artificial intelligence, and machine learning algorithms for mission systems. Responsibilities span requirements generation, software and hardware integration, troubleshooting, verification, human-machine interfaces, and technical documentation. The role also supports hardware prototyping, mechanical and electronics design, qualification testing, design reviews, supplier coordination, and program planning. Work may involve robotics, computer vision, signal processing, and multi-agent systems, with travel to remote locations up to 25% of the time.

Leads development, integration, testing, validation, and deployment of AI and machine-learning capabilities for radar, communications, and electronic warfare signal-processing applications. Drives technical strategy, leads cross-functional engineering teams, integrates models into mission-critical systems, and communicates technical approaches to customers and stakeholders. The role also supports requirements development, software integration, field transition, experimentation, and technical roadmaps.

Principal AI/ML Engineer responsible for architecting and leading enterprise-scale cognitive computing and conversational AI solutions. The role involves NLP model development, MLOps automation, cloud deployment, REST API integration, scalable software delivery, architecture direction, team leadership, coaching, and cross-functional stakeholder collaboration.

Designs, tests, evaluates, and productizes AI/ML algorithms for mission-focused business challenges. Responsibilities include selecting and analyzing data, training and improving models, researching ML methods, ensuring data and output quality, developing explainability and resiliency criteria, overseeing system integration and testing, and deploying scalable solutions across commercial cloud architectures. The role requires advanced AI/ML expertise, software engineering skills, and strong knowledge of data modeling, ontologies, semantics, and knowledge representation.

Design, develop, test, and productize AI and machine learning algorithms. Responsibilities include selecting and analyzing data, training and improving models, conducting test and evaluation protocols, overseeing system integration, researching AI/ML tools, converting prototypes into scalable solutions, and validating data and model quality. The role requires a Top Secret/SCI clearance with favorable polygraph, U.S. citizenship, five years of machine learning deployment experience, and an advanced technical degree or equivalent experience.

Lead the Machine Learning Ranking team within ZipRecruiter’s Marketplace organization. Manage and develop senior data scientists and machine learning engineers, shape product roadmaps with Product and Decision Science, guide technical decisions, build and deploy ML pipelines, and monitor model speed and accuracy. The role requires strong software engineering leadership, ML expertise, coding ability, and experience applying embeddings, reproducible pipelines, and established ML frameworks to business problems.

Develop, train, test, and evaluate AI/ML models and supporting software for aerospace and defense applications. Research and implement technical approaches, design reproducible experiments, analyze performance, investigate failures, and translate mission objectives into AI/ML tasks. Build simulation environments, data-processing tools, and evaluation workflows; contribute to multi-agent autonomy projects; integrate components into software frameworks; and communicate technical findings to teammates, leads, and customers.

Contribute to HP IQ’s AI/ML platform across model inference optimization, agent orchestration, and model fine-tuning. Responsibilities include profiling and accelerating models on resource-constrained devices, developing LangGraph or LangChain workflows, adapting pretrained language models, evaluating experiments, collaborating cross-functionally, contributing to libraries or open-source projects, and improving platform reliability.

Build and deploy machine learning models for risk and fraud detection. Lead technical efforts in the risk and fraud domain, use SQL and Python for data preparation and production code, design statistically sound experiments, evaluate model performance, and partner with Operations to respond to evolving financial threats.

Develops and validates predictive, machine-learning, and deep-learning models; performs exploratory statistical analysis; processes large datasets using GPU, distributed, and cloud computing; creates visualizations and recommendations; and collaborates cross-functionally to improve products and processes. The role requires Python, TensorFlow, PyTorch, large-dataset management, and eligibility for a Secret clearance as a U.S. citizen.

Develops, integrates, tests, and deploys autonomy, artificial intelligence, and machine-learning algorithms for mission systems. Translates requirements into software, builds perception, planning, controls, and human-machine interface capabilities, and supports the full development lifecycle. Responsibilities include model training, software integration, troubleshooting, debugging, test evaluation, and documentation. The role also collaborates across teams using Agile practices and supports mission-critical product and platform development.