Top Senior Machine Learning Jobs

As a Senior Applied ML Engineer at Macroscope, you will design, build, and optimize ML/AI systems, leading the AI Code Review process and ensuring integration with backend systems.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.

The Senior Staff Machine Learning Engineer will lead the development of GEICO's AI-driven virtual agent platform, overseeing technical design, cross-functional collaboration, strategic planning, and mentoring within the team throughout project lifecycles.

The Machine Learning Engineer will apply data science and machine learning practices to solve problems, develop prototypes, and communicate insights to stakeholders in a dynamic environment.

Lead and grow the AI/ML Engineering team, manage core AI/ML platform development, ensure production-grade service operational excellence, and develop product strategies.

Own end-to-end ML lifecycle for pricing and underwriting: improve model accuracy and coverage, reduce infrastructure cost, deploy and monitor production models on AWS, run experiments/backtests, and collaborate with domain experts to inform model improvements and product APIs.

Design, train, evaluate, optimize, and deploy production ML models including recommendation, ranking, personalization, and adaptive decision systems. Apply reinforcement learning, contextual bandits, offline policy evaluation, causal inference, and experimentation to improve creator-facing products. Build scalable training, evaluation, deployment, and inference pipelines; work with logged interaction data; collaborate with Product, Engineering, and Analytics to translate customer problems into production ML solutions.

The role involves leading machine learning model design, AI development, data analysis, and enhancing digital workplace experiences at CVS Health.

Design and optimize high-performance ML-focused ASIC microarchitectures. Translate researcher requirements into RTL, specify execution units and datapath elements, perform power/area/performance tradeoffs, and collaborate with verification teams to ensure functional correctness and target ML performance.

Design, build, and maintain scalable ML models and data pipelines to automate logistics processes. Own end-to-end projects from problem definition to production deployment and monitoring. Collaborate with engineering, mentor ML teammates, review code, and evaluate tooling to improve infrastructure and workflows that deliver customer value.

Conduct original ML research (LLMs, RL, multi-agent systems, diffusion/inverse models, trustworthy AI), publish and patent, build production-ready prototypes with GPU/HPC acceleration, collaborate across teams, and mentor junior researchers to translate research into deployable solutions for data center optimization, energy, and scientific computing.

Build and productionize ML models and data infrastructure to analyze millions of chaos engineering experiments, detect and explain failures, recommend automated remediation, and integrate AI-driven failure analysis into Gremlin's product using techniques like causal inference, graph ML, time-series modeling, and reinforcement learning.

Design, build, and operate large-scale, low-latency recommendation systems (retrieval, ranking, multi-objective optimization). Drive offline training, online inference, A/B testing, and metric analysis. Apply LLMs and foundation models for content understanding, user intent, and feature generation. Rapidly prototype AI/LLM ideas and own projects from MVP to production, ensuring data quality and system performance.

Lead technical direction and architecture for large-scale personalization and recommendation systems. Drive adoption of LLMs/foundation models, own retrieval-to-ranking pipelines, model lifecycle management, experimentation, and production scaling. Mentor teams and shepherd 0→1 AI platform initiatives into maintainable production.

As a Senior Principal Machine Learning Engineer, you'll lead technical direction, drive scalable model development, and mentor teams in a dynamic environment, optimizing Disney's ad technology.

Lead development and productionization of personalization and recommendation algorithms (ranking, retrieval, content/user understanding, post-ranking). Apply modern AI/LLM techniques, design and run offline and online experiments, drive technical vision and roadmap, collaborate with engineering/product/data teams, and build maintainable, production-grade ML systems at scale.

Lead the development of machine learning algorithms for personalization and recommendations in Disney's streaming products while collaborating with product and engineering teams.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.

Develop and implement reinforcement and supervised learning algorithms for robotic manipulation, build simulations and domain randomization, optimize model architectures and data pipelines for sample efficiency, and deploy, evaluate, and debug policies on physical robots.

The Staff ML Engineer will define and build ML capabilities, focusing on production systems, strategy, and infrastructure in a cybersecurity context.

As a Senior Machine Learning Engineer (Tech Lead), you'll lead the ML technical direction, design architectures for loco-manipulation, mentor team members, and manage cross-functional partnerships while writing production-quality code.

The role involves deploying and optimizing LLMs, designing the ML serving stack, and ensuring high-performance GPU services for production readiness.

As a Staff Machine Learning Engineer, you will design, build, and deploy ML systems, optimize performance, and collaborate with teams to integrate AI solutions.

As an Applied Machine Learning Engineer, develop, deploy, and optimize machine learning models while collaborating with cross-functional teams to enhance customer solutions.

The Staff AI/ML Engineer will develop and prototype AI/ML algorithms, analyze datasets, and mentor team members in an interdisciplinary environment.