Top Machine Learning Jobs

Apply advanced quantitative and computational methods to build and deploy machine learning, optimization, and statistical models on large structured and unstructured consumer data. Drive full modeling lifecycle from problem definition to communication of results and recommendations, present findings to senior stakeholders, and act as a subject-matter expert while contributing new ideas to the team.

Build and validate ML, optimization, and statistical models on large structured and unstructured consumer datasets; generate insights and actionable recommendations; communicate results to senior stakeholders; contribute across the full modeling lifecycle and advance team knowledge.

Design, build, and productionize machine learning models and data pipelines for recommendation, bidding, and forecasting. Collaborate with product and engineering teams, evaluate and monitor model performance, maintain high-quality tested code, prototype research ideas, and run experiments. Hybrid schedule: four days in-office, one remote.

Drive technical strategy for the Personalization Platform, develop recommendation systems, maintain ML workflows, and innovate AI solutions for customer engagement.

The Lead Machine Learning Engineer designs and implements ML applications, builds data pipelines, collaborates with teams, and ensures performance and governance of ML systems.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

Lead design and deployment of production-grade ML systems (ranking, retrieval, LLMs). Drive architecture decisions, run experiments and evaluations, build scalable feature pipelines and monitoring, mentor engineers, and collaborate cross-functionally to integrate AI into products and scale ML capabilities.

Design, build, and operate scalable ML systems and services end-to-end: data sourcing, feature engineering, model development, deployment, monitoring, and operational support. Collaborate across product, data, and engineering, drive ML best practices, and mentor other engineers to improve model reliability, performance, and observability.

Design, develop, and deploy end-to-end ML models and systems for pricing and revenue optimization. Build data pipelines, feature engineering, model training, evaluation, and production monitoring. Partner with product, engineering, and analytics to define success metrics and run experiments. Apply causal inference and optimization techniques to improve business outcomes and enable scalable model-serving and reuse.

Lead research, design, and production deployment of large-scale search and recommendation ML systems. Architect multi-modal retrieval and multi-stage ranking pipelines, advance intent understanding and personalization, develop sequential recommenders, design offline/online evaluation (A/B, counterfactual), collaborate with engineering/product teams, and mentor scientists to deliver production-ready AI solutions.

Lead research, design, and production deployment of large-scale search and recommendation ML systems, including multi-modal retrieval, multi-stage ranking, personalization, sequential recommenders, evaluation (A/B testing, counterfactuals), and mentoring scientists while partnering with engineering and product teams.

Lead a team developing ranking, recommendation, and intent models for bundled travel products. Define ML roadmaps, oversee model design, evaluation, A/B testing, causal inference, and productionization. Partner with engineering and product to deploy scalable, observable systems, and mentor ML scientists to raise technical rigor and impact.

Design, build, and scale production ML systems for distribution and supply; own end-to-end ML feature delivery from data and modeling to deployment and monitoring; collaborate with cross-functional teams; improve observability, reliability, and operational excellence; mentor engineers and apply advanced ML tooling and platform practices.

Design and deploy end-to-end ML models for ranking, recommendations, search, and personalization. Build scalable data pipelines, monitoring, and evaluation frameworks. Collaborate with product, analytics, and engineering to translate business problems into ML solutions and measure impact via A/B tests and online/offline evaluations.

Design, implement, and deploy end-to-end machine learning solutions to improve post-booking customer experience. Build and validate models, run experiments (A/B), measure business impact, and collaborate with product, engineering, and stakeholders to productionize and monitor ML systems.

Drive end-to-end ML solutions for search, ranking, recommendations, and personalization. Build production model pipelines, scalable data pipelines, monitoring, A/B testing, and collaborate with cross-functional teams to translate business needs into measurable, reliable AI systems.

Design, implement, and deploy end-to-end ML models and data pipelines for ranking, recommendations, search, and personalization. Build scalable data quality checks, monitoring, and evaluation (A/B tests, offline/online) to ensure reliable, fair, production-grade AI. Collaborate with product, analytics, and engineering to translate business needs into ML solutions and drive iterative improvements.

Lead the ML roadmap and build production ML systems for CRM personalization. Own end-to-end modeling, deployment, experimentation, and measurement. Partner with marketing, analytics, and engineering, communicate insights to stakeholders, and mentor other scientists while promoting best practices and reproducibility.

Design, develop, and deploy end-to-end ML models and systems for pricing and revenue optimization. Build data pipelines, feature engineering, model training, evaluation, and production monitoring. Partner with product and engineering to define metrics, run experiments, and apply causal inference and optimization techniques to improve business outcomes.

Design, implement, benchmark, and optimize CUDA-based GPU kernels and custom operators for on-vehicle ML inference. Build tooling for profiling and debugging, integrate kernels into the ML runtime, collaborate with compilers and architecture teams, and deliver high-performance, production-grade libraries meeting strict latency, throughput, and reliability targets.

Design, train, and productionize machine learning models for fraud and identity risk across the full ML lifecycle. Research new fraud types, perform feature engineering and labeling, write production-ready code for real-time decisioning, monitor models, and provide analyses to inform product, sales, and data acquisition.

Lead experimental ML initiatives: design, train, and deploy multi-modal models and production pipelines; evaluate and prototype emerging ML technologies and agentic workflows; establish AI governance, mentoring, and knowledge sharing; translate research into business impact and thought leadership.