Top Machine Learning Jobs

Designs, develops, and deploys computer vision and machine learning pipeline components for GEOINT data. Responsibilities include synthetic data generation with generative AI, data engineering, model training and production deployment, cloud and Kubernetes workflow operations, system architecture, production support, and briefing teams on project progress and results.

Leads multiple AI/ML engineering teams delivering enterprise AI, generative AI, agentic AI, and automation solutions. Oversees platform strategy, software delivery, responsible AI governance, cloud adoption, and operational excellence. Partners with product and engineering leaders on healthcare solutions, mentors technical teams, and manages AI initiatives spanning claims automation, conversational AI, predictive analytics, and intelligent workflows.

Design, develop, deploy, and optimize scalable AI/ML solutions, including LLM applications, RAG pipelines, AI agents, and MLOps workflows. Build production-grade ML pipelines, automation frameworks, monitoring, evaluation, and governance capabilities using Python and AWS services. Collaborate with technical and business teams to integrate reliable AI solutions into production systems and continuously improve model performance, scalability, and compliance.

Build and deliver cloud-based AI and machine learning solutions for ServiceNow’s enterprise search platform. Develop high-quality product features using efficient algorithms, collaborate with developers, product managers, and quality engineers, and own software through design, implementation, testing, and deployment. Ensure solutions are reliable, scalable, performant, modular, and well tested while improving intelligent search and enterprise workflow experiences.

Leads the strategy and execution of AI model performance and on-vehicle inference for autonomous driving. Responsibilities include managing engineering leaders, setting performance budgets, optimizing models and GPU systems, establishing benchmarking and profiling practices, detecting regressions, building performance dashboards, and guiding cross-functional architecture and production decisions. The role also oversees hiring, coaching, staffing, KPIs, technical leadership development, and trade-offs across latency, memory, throughput, accuracy, power, and numerical parity.

Designs, develops, evaluates, and deploys cloud-based machine learning and AI solutions for enterprise services. Responsibilities include training and iterating on supervised, unsupervised, deep learning, NLP, and LLM models; converting research into production systems; partnering with product teams; and owning software from design through monitoring. The role may also involve high-scale ML systems, LLM fine-tuning, retrieval-augmented generation, agentic AI, and MLOps pipelines.

Leads end-to-end generative and agentic AI initiatives, from research and experimentation through production deployment. Designs foundation-model solutions, evaluation frameworks, semantic modeling standards, and responsible AI controls. Partners with product, engineering, risk, and business stakeholders to define outcomes, optimize reliability and performance, and deliver scalable AI services. Mentors engineers and data scientists while communicating technical recommendations to diverse audiences.

Leads a team developing AI capabilities for CrowdStrike’s cross-platform endpoint sensor. Owns the AI-on-endpoint roadmap, including machine learning model operationalization, telemetry data pipelines, on-device inference, architecture, feature delivery, and cross-functional collaboration. Manages hiring, mentoring, engineering quality, testing, performance, and releases across Windows, macOS, and Linux sensor platforms.

Leads the design, development, deployment, and operationalization of machine learning, GenAI, and LLM-based systems. Architects scalable AI solutions, guides technical teams, partners with stakeholders, and ensures model performance, transparency, regulatory compliance, and responsible AI practices. The role also drives proof-of-concepts, model evaluation, MLOps, cloud-based ML workloads, prompt engineering, and emerging AI research translation.

Leads advanced analytics, data engineering, machine learning, NLP, and AI solutions to detect employee misconduct, fraud, and other risks. Designs and operationalizes production-grade solutions using Python, SAS, structured and unstructured data, and generative AI tools. Establishes scalable frameworks, evaluates models, analyzes sensitive data, and provides technical leadership while ensuring regulatory, governance, security, and model-risk compliance.

Leads advanced analytics, machine learning, NLP, and AI solutions for employee misconduct, fraud, regulatory, and reputational risk detection. Acquires, engineers, models, and prepares structured and unstructured data, moving solutions from prototypes into production using Python and SAS. Establishes reusable development frameworks, evaluates emerging technologies, provides technical leadership, and ensures compliance with governance, model risk, security, legal, and regulatory requirements.

Lead end-to-end analytics and machine learning solutions for detecting employee misconduct, fraud, and related risks. Acquire, engineer, model, test, and deploy structured and unstructured data solutions using Python and SAS. Develop reusable frameworks, production pipelines, and evaluation methods while applying NLP, generative AI, and AI-assisted development. Provide technical leadership, interpret risk signals for senior stakeholders, and ensure compliance with governance, model risk, security, legal, and regulatory requirements.

Leads end-to-end data engineering, advanced analytics, machine learning, NLP, and AI solution development for employee misconduct, fraud, and conduct-risk detection. Acquires and models structured and unstructured data, moves prototypes into production, establishes reusable Python and SAS frameworks, evaluates emerging technologies, and delivers scalable monitoring solutions. Provides technical leadership while ensuring compliance with governance, model risk, information security, legal, and regulatory requirements.

Develop and operationalize advanced analytics, machine learning, NLP, and AI solutions to detect employee misconduct, fraud, and other risks. Lead data acquisition, engineering, modeling, testing, and production deployment using Python and SAS. Establish reusable frameworks and pipelines, evaluate emerging technologies, analyze sensitive large-scale data, and communicate actionable insights to senior risk and business stakeholders while meeting governance, regulatory, security, and model risk requirements.

Develop and productionize machine learning systems for personalization, recommendation, ranking, retrieval, traveler understanding, and sequential modeling. Lead end-to-end scientific work including problem formulation, data exploration, feature engineering, model development, experimentation, evaluation, and operationalization. Collaborate with engineering, product, and business stakeholders on system and API design, mentor other scientists, influence technical direction, and apply advanced deep learning and foundation-model techniques at scale.

Develop and optimize deep learning algorithms for resource-constrained embedded audio devices. Build software prototypes, deploy models, research efficient techniques such as quantization and pruning, evaluate models, and assess neural-network hardware accelerators. The role focuses on audio ML for headphones, earbuds, speakers, soundbars, and wearables, requiring embedded C/C++, Python, deep learning frameworks, and collaboration across teams.

Design, prototype, and evaluate machine learning models; develop maintainable training and inference code; conduct data preparation, feature engineering, statistical analysis, and error analysis; and contribute to ML system, API, and data modeling. Collaborate with scientists, engineers, mentors, and product partners to define metrics and build production-aware, fair, and scalable ML solutions.

Design, prototype, and evaluate machine learning models for travel products. Build maintainable training and inference code, conduct statistical analysis and feature engineering, participate in ML system and API design, and collaborate with data scientists, engineers, mentors, and product partners. Interns will contribute to research and product experiments involving scalable services, A/B testing, fairness, and AI-enabled solutions.

Develop end-to-end data and machine learning capabilities for Special Operations users. Responsibilities include building scalable pipelines, transforming large datasets, applying advanced analytics and machine learning, deploying models and inference services, developing APIs, and delivering analyst-facing applications. The role requires collaboration with mission users, analysts, engineers, and technical leadership in a customer-site environment.

Designs, scales, and leads machine learning systems that detect harmful content and bad actors, support content moderation, and promote platform integrity. Provides technical direction across the ML organization, partners with cross-functional leadership, improves ML infrastructure and operational excellence, and delivers performant, scalable models using modern deep learning techniques.

Develop and deploy signal-processing algorithms and machine-learning models that convert wearable sensor data into real-time physiological insights. Responsibilities include data analysis, model training, Python prototyping, optimized C/C++ implementation, embedded firmware integration, and production validation. The role requires collaboration with firmware, hardware, data science, and domain teams to optimize accuracy, power, memory, compute, and latency while conducting rigorous offline, laboratory, real-world, and on-device testing.

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Own the AI roadmap for battery manufacturing, identifying, benchmarking, piloting, and scaling use cases that improve yield, quality, cost, and launch speed. Build proofs of concept and reference architectures for process optimization, defect vision, yield genealogy, and predictive maintenance. Establish reusable data, labeling, and model standards; evaluate vendors; guide engineering teams; align with IT and OT on security and scalability; and apply generative and agentic AI to plant workflows.

Develop and improve reliable LLM and deep learning capabilities, build scalable real-time machine learning models, advance Responsible AI practices, and define technical strategies for deep learning, RAG, and LLMs. Collaborate with AI scientists and engineers, conduct ML and reinforcement learning research, fine-tune models, and drive adoption of AI best practices across service 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.