Top Senior Machine Learning Jobs

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.

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.

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, 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 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.

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.

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.

Lead and grow a team of ML and software engineers to build large-scale indexing, retrieval, and ranking systems for Snapchat ads. Define architecture and evaluation frameworks, collaborate with product and data science, measure outcomes, and raise engineering and ML standards while shaping Snap's broader ML strategy.

Design, build, and productionize causal ML models (uplift, heterogeneous treatment effects), analyze A/B tests and quasi-experiments, collaborate with product and engineering, ensure scalable, maintainable infrastructure, and balance model complexity with interpretability and business impact.

Build and productionize statistical and ML models to optimize advertiser performance and business goals in mobile ad tech. Tune optimization, model competition and dynamic environments, design experiments, develop budget retention/growth and bidding systems, and collaborate across engineering and business teams while ensuring explainable, well-tested solutions.

Design, build, and operate scalable, low-latency search and retrieval systems powering Atlassian products. Develop and serve production ML models (neural rankers, embeddings, rerankers) using PyTorch and Triton. Implement vector indexing and RAG/agentic retrieval pipelines, ensure observability and cost optimization, manage on-call/runbooks, and collaborate cross-functionally while mentoring junior engineers.

The role involves designing and implementing machine learning solutions for geospatial data, participating in multi-discipline teams, and mentoring internal teams while ensuring efficient project execution and client communication.

Design, develop, and deploy machine learning and LLM-based models for credit decisioning and fraud across the customer lifecycle. Build explainability, fairness, and responsible-AI tooling, collaborate with risk, governance, and engineering teams, and operationalize models with monitoring, drift detection, and auditability.

The Senior AI/ML Engineer will enhance developer productivity by improving Python workflows, leading projects, mentoring engineers, and driving best practices.

Lead design, build, and productionize large-scale ML systems (including RL-based recommenders). Collaborate with Product and Data Science to create data pipelines, cloud architectures, CI/CD, model retraining, monitoring, and Responsible/Explainable AI practices. Mentor and lead teams to deliver performant, resilient ML applications across web, mobile, and email.