Top Senior Machine Learning Jobs

Lead development and deployment of credit, account-management, and fraud ML models using embeddings, transformers, and classical approaches. Own data pipelines, feature engineering from structured and unstructured sources, production monitoring, and cross-team integration while shaping technical strategy and mentoring Juniors.

Lead ML strategy and integrate ML into real-time game systems and toolchains. Design and deploy LLM techniques, build training and inference pipelines for on-device and real-time models, prototype ML-driven gameplay features, analyze telemetry for personalization, and mentor engineers.

Design and implement production-grade multi-agent LLM systems and agent workflows for advisor-facing tools. Build distributed, observable services (model serving, vector DBs, RAG) with memory/state management, validation, and compliance. Partner with product and engineering to optimize for latency, cost, and reliability, and establish evaluation frameworks to ensure grounded, auditable AI-driven insights for portfolio and advisor workflows.

The Senior Engineer in AI & ML will design and productionalize machine learning models, collaborating with Data Scientists to implement scalable solutions and enhance customer experiences at CarMax.

As a Lead Machine Learning Engineer, you will operationalize ML models, collaborate with teams, solve complex problems, optimize data pipelines, and implement best practices in ML deployment.

Design, build, and deploy scalable ML models for R&D and expand into clinical/regulatory use cases. Partner with cross-functional teams, ensure data availability and governance, evaluate vendor and internal solutions, and maintain explainability, traceability, and reproducibility within enterprise architecture.

Design and implement agent harnesses and post-training pipelines, build evaluation benchmarks, develop RL environments and reward models, and debug/optimize multi-GPU training runs to improve LLM capabilities for agentic applications.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

The Staff Machine Learning Engineer will enhance Quora's recommendation systems, lead machine learning projects, and own end-to-end ML processes.

The role involves improving recommendation systems using machine learning, developing end-to-end ML systems, and collaborating with engineers.

The Principal Machine Learning Engineer will lead hands-on model development, oversee predictive modeling, and drive AI initiatives, ensuring models are effective and operationally sound.

The Senior Engineer, Machine Learning will develop novel algorithms using biomedical datasets, collaborate with experts, and ensure data quality through algorithmic solutions.

The Machine Learning Engineer designs, builds, deploys, and maintains machine learning models, optimizing their performance for business value. Responsibilities include managing ML pipelines and collaborating with data teams to implement MLOps best practices.

The role involves developing software solutions with a focus on machine learning, ensuring scalability, performance, and real-time system communication, while promoting best practices and collaboration within a technical team.

The Senior Machine Learning Engineer will design, build, and deploy scalable machine learning solutions, collaborate with teams, and perform data transformation and monitoring.

The Principal Machine Learning Engineer leads the design and development of advanced ML models, overseeing their lifecycle and collaborating with teams to optimize performance and ensure regulatory compliance.

The Applied Scientist will develop and enhance machine learning models, design A/B tests, evaluate performance, and collaborate with stakeholders to enhance decision-making and operational workflows in healthcare.

Lead the evolution of Unity's Ads Experimentation platform as a senior machine learning engineer, focusing on optimizations and statistical methodologies for ad performance.

As a Senior Machine Learning Engineer, you'll design and implement large-scale ML systems to enhance ad relevance and performance, mentor engineers, and collaborate with cross-functional teams in Mandarin.

As an Applied ML Software Engineer, you will design and implement ML models, manage cloud infrastructure, and optimize backend systems for AI-driven content discovery and recommendations.

The Lead Machine Learning Engineer will optimize AWS SageMaker, manage ML pipelines, develop infrastructure as code with Terraform, build CI/CD pipelines, and collaborate with data scientists to deploy models in production.

The role involves leading ML strategy, architecting systems, designing algorithms, collaborating with teams, and mentoring engineers in AI for enterprise analytics.

Design, develop, and implement machine learning models and collaborate with teams to enhance services with AI/ML solutions.

The Senior Machine Learning Engineer will design, deploy, and operate ML systems for advertising, collaborating with cross-functional teams to enhance performance and reliability. Responsibilities include mentoring, promoting best practices, and owning ML project lifecycles.

As a Senior Machine Learning Engineer, you will build and operate scalable ML systems, oversee projects from inception to deployment, and improve system performance while collaborating with cross-functional teams and mentoring junior engineers.