Top Senior Machine Learning Jobs

Leads the design, development, and scaling of AI-enhanced applications, APIs, microservices, and automation frameworks. Integrates LLMs, generative AI, computer vision, chatbots, agentic workflows, and autonomous systems into production products. Establishes AI-first engineering practices, optimizes model costs and performance, implements observability, and mentors engineers. The role also influences architecture, technical strategy, business trade-offs, and adoption of AI-powered development tools in an agile environment.

Develop and ship end-to-end machine learning models for autonomous driving behaviors across L2-L4 systems. Own data curation, training, evaluation, experimentation, and on-road validation. Improve model performance through iterative training cycles while addressing real-world safety, quality, and deployment constraints. The role may involve reinforcement learning, transformer networks, automotive systems, model compression, and embedded deployment.

Lead PayJoy’s ML platform organization and manage Staff and Senior ML and software engineers. Define the long-term roadmap for ML infrastructure, feature stores, model serving, ETL, internal APIs, deployment, monitoring, and MLOps. Partner with Risk, Fraud, Engineering, Product, and Data Science stakeholders to translate business goals into scalable technical solutions. Mentor engineering leaders, ensure production reliability and compliance, identify operational bottlenecks, and guide adoption of technologies such as LLMs and graph databases.

Designs, evaluates, and improves ranking and recommendation systems through experimentation, feature engineering, and production machine learning. Builds reliable data and feature pipelines, incorporates multimodal signals, manages uncertainty, calibration, and model drift, and monitors system performance. Partners cross-functionally with Engineering, Product, Business, and Analytics to deliver scalable, latency-sensitive solutions with measurable business impact.

Design, develop, and deploy scalable AI/ML solutions and agentic LLM systems for payments. Build data pipelines, perform analysis, partner with Product, Technology, Risk and Compliance, track model performance and regulatory documentation, and translate model outcomes into business impact.

Develop, deploy, and maintain scalable AI/ML solutions for payments including fraud detection and customer experience. Build large-scale data pipelines and feature stores on AWS, fine-tune LLMs/transformers, run experiments, monitor model performance, ensure regulatory compliance, and translate model outcomes into business metrics while working cross-functionally.

Lead end-to-end development and production deployment of causal and predictive ML solutions for private banking. Frame causal problems, build uplift/HTE models and observational causal studies, drive MLOps, monitor model quality, mentor data scientists, and translate causal/LLM research into production in partnership with risk, compliance, and business stakeholders.

Lead end-to-end delivery of production AI/ML capabilities for payments (fraud, anomaly detection, automation, routing). Design and deploy neural/Transformer models, optimize for latency/scale/cost, define evaluation and monitoring, ensure model governance and audit readiness, collaborate with Product/Engineering/Risk, and mentor teams while communicating trade-offs to senior stakeholders.

Build and automate data-flywheel systems for autonomous robots, converting large-scale robot experiences into high-quality, versioned training datasets. Responsibilities include data processing, filtering, labeling, auto-labeling, offline inference, hard-example selection, validation, reproducible dataset generation, and incremental training-data pipelines. Partner with researchers and platform teams to productionize experimental workflows and improve the cycle from robot deployment to usable ML data.

Develop and integrate large-scale data applications using Accumulo, Hadoop, Kafka, Spark, Yarn, Python, and JavaScript. Implement predictive analytics, graph algorithms, and machine learning models; design client/server systems and integration points; improve development workflows and support analytic toolchains for Intelligence Community missions.

The Senior Machine Learning Engineer will develop core ML models, manage data pipelines, and collaborate with stakeholders to deliver scalable ML solutions.

The Senior Machine Learning Engineer will design innovative machine learning models for drug discovery, leveraging large-scale single-cell datasets and collaborating with ML Scientists to develop optimized models.

Lead the development of machine learning solutions for video personalization, enhancing user experience through data-driven insights and collaboration across teams. Drive technical strategy in building scalable systems and partnerships to innovate customer recommendations.

Develop and improve machine learning models for Pinterest's various product surfaces, utilizing deep learning and advanced data processing.

Lead product strategy for AI-powered personalization and recommendation capabilities. Define requirements, partner with Data Science and Engineering to build, test, launch and optimize models, monitor performance, run experiments, prioritize enhancements using analytics, and communicate results to stakeholders while ensuring scalability, compliance and strong customer experiences.

Design, implement, and deploy machine learning models and systems for recommendations and related problems. Collaborate with engineers, data scientists, and product partners to analyze data, build proofs-of-concept, develop ML/optimization models, write production-quality code, and evaluate models against business goals.

Lead development of online mapping perception algorithms for Aurora’s autonomous driving system. Research and build deep learning models, develop verification and validation techniques, and collaborate with mapping, simulation, and safety teams. Build infrastructure and tests supporting deployment decisions, then integrate, test, and deploy production-ready solutions into the Aurora Driver codebase.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Lead a team to measure and improve enterprise search relevance and ranking using ML and LLM/RAG techniques. Manage end-to-end model development, evaluation, deployment, cross-functional collaboration, and team growth to ensure scalable, production-grade search quality.

The Senior Machine Learning Researcher will design experiments, research new algorithms, and collaborate on deploying models for computer vision and robotics applications.

Design, develop, deploy, and operate enterprise AI/ML solutions, including agentic systems, multimodal models, RAG, and responsible AI applications. Own MLOps and LLMOps practices, CI/CD, model versioning, observability, evaluation, monitoring, deployment, and lifecycle management. Lead architecture and technical decisions, partner with cross-functional stakeholders, resolve complex technical challenges, communicate strategy to leadership, and evaluate emerging AI technologies.

Design, build, and productionize core AI systems for document understanding, forecasting, and agentic workflows. Handle messy data ingestion, evaluation, monitoring, anomaly detection, entity reconciliation, and backtesting to deliver reliable, product-focused ML solutions.

Founding ML engineering hire to design and build production ML systems: training and inference pipelines, serving, feature workflows, monitoring, retraining, governance, and operational practices. Partner across engineering, data, product, and applied science to productionize models, implement observability, prevent degradation, and set platform standards and hiring bar for future ML engineers.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Design, implement, and deploy production-scale machine learning models to evaluate disputes, chargebacks, and fraud likelihood. Build and manage orchestration layers for multiple models, collaborate with stakeholders to integrate partner/customer data, and improve automated and supervised decision-making for a product-focused ML environment.