Top Senior Machine Learning Jobs

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.

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 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.

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.

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.

Build and lead production machine learning capabilities for Snapchat+ subscription growth and monetization. Responsibilities include developing personalization, ranking, propensity modeling, offer decisioning, retention, and lifecycle optimization systems; translating ambiguous business problems into ML solutions; conducting experimentation; deploying scalable services; and establishing technical direction. The role partners with Product, Data Science, Backend, and Mobile Engineering teams while measuring impact on revenue, subscriber value, and retention.

Develop production-ready machine learning and signal-processing algorithms for wearable physiological sensor data. Responsibilities include designing and evaluating deep learning models, analyzing large datasets, optimizing algorithms for embedded edge deployment, and balancing accuracy, power, memory, latency, and scalability. The role owns projects from research and prototyping through validation, deployment, monitoring, and continuous improvement while collaborating with scientists, engineers, hardware experts, and product teams.

Leads Adyen’s Credit Risk Modeling team, overseeing the design, productionization, deployment, monitoring, and improvement of machine learning models and rule-based decision systems for credit underwriting. The manager provides hands-on technical leadership, mentors machine learning scientists and data engineers, shapes architecture and product roadmaps, collaborates cross-functionally, recruits team members, and builds an inclusive, high-performing engineering culture.

Leads AI strategy, teams, and production-scale machine learning initiatives. Responsibilities include establishing MLOps practices, integrating AI technologies, guiding build-versus-buy decisions, delivering intelligent solutions, implementing governance and responsible AI, and driving adoption across products and enterprise platforms. The role requires cross-functional leadership, technical expertise in machine learning and advanced AI methods, stakeholder influence, mentoring, and delivery of measurable business outcomes.

Leads development, optimization, implementation, and evaluation of AI/ML algorithm solutions for complex datasets and national security applications. The role focuses on computer vision, 3D modeling, multimodal sensor fusion, open-set recognition, and deployment across cloud and edge environments. Responsibilities include collaborating with researchers, engineers, and academic partners; transitioning prototypes into operational capabilities; and communicating results to technical and non-technical audiences.

Develop and deploy machine learning and LLM capabilities for agentic search, query understanding, search planning, and retrieval. Build evaluation datasets, improve model behavior through experimentation and fine-tuning, and optimize quality, latency, cost, and reliability. Collaborate across search, product, infrastructure, and AI teams while owning projects through production and mentoring engineers.

Develop and deploy scalable machine-learning solutions, particularly deep-learning forecasting models for company top-line metrics. Lead the full ML lifecycle from problem definition and experimentation through production deployment and improvement. Design production-grade architectures, evaluate and improve models, collaborate with cross-functional teams, explain technical concepts to varied audiences, and mentor junior engineers.

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.

Leads machine learning and software engineering teams building models, agentic systems, data pipelines, and ML infrastructure for advertising integrity and recommendations. Owns technical strategy, roadmap, model quality, reliability, privacy, and responsible AI. Partners cross-functionally to deliver measurable outcomes, scales LLM-powered ad review, contributes technically, and hires and mentors high-performing engineers.

Develop production LLM-powered agents and machine learning solutions for customer risk identification and threat detection. Translate threat-research methods into agent logic, build data requirements and pipelines, evaluate precision and recall, backtest and tune models, and deploy and monitor reliable solutions using CI/CD. Collaborate with threat researchers and data engineers while addressing production reliability, observability, data quality, cost, latency, and accuracy.