Top Senior Machine Learning Jobs

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

The Manager, Machine Learning Engineer leads AI/ML initiatives, manages teams, oversees model development, and ensures alignment with business objectives.

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.

Design and deploy ML models and platforms for Axon products, collaborating with scientists and PMs to build POCs, train/evaluate/test models, enable secure privacy-preserving deployment to cloud and devices, and set standards for ethical AI.

Design, build, deploy, and optimize scalable production ML models (recommendation systems, optionally computer vision). Collaborate with engineers, product, and stakeholders to improve customer experience and evolve CoStar's cloud ML platform.

As the founding Machine Learning Engineer, you'll own end-to-end credit risk modeling, from data pipeline design to production deployment, collaborating with cross-functional teams to shape modeling decisions and define future hires.

Build, optimize, and deploy production-grade machine learning systems for sports analytics. Improve data pipelines, feature engineering, model training, evaluation, and low-latency prediction services. Collaborate with DevOps and Data Engineering to scale workloads on Kubernetes, maintain cloud-native EDW/ETL solutions, and promote software development best practices.

The role involves developing scalable ML algorithms, optimizing LLM inference performance, managing model serving pipelines, and collaborating with data engineers for efficient knowledge graph generation.

The Machine Learning Engineer will build tools for interpretable AI, optimize infrastructure, and integrate workflows into products, ensuring reliability and performance.

Lead design and implementation of production ML systems for low-latency ad use cases (bidding, ranking, pacing, fraud). Build training and feature pipelines, real-time inference services, and ML team. Partner with engineering and product, own model experimentation, evaluation, monitoring, and deployment at scale.

Design, architect, develop, integrate, and support production ML solutions on AWS. Collaborate with product and domain experts, mentor junior engineers, guide feature implementation, and use metrics to drive decisions while writing production-quality code.

The role involves developing scalable machine learning applications, collaborating across teams, and enhancing data products at Poshmark.

Design and build reliable, scalable data analysis libraries, applications, and pipelines; collaborate on architecture and code reviews; prototype ML solutions; own full software development lifecycle; dive into engineering problems with domain experts to support launches and Starlink operations.

As a Staff ML Engineer, you'll lead complex model quality improvements in clinical AI, spanning research and engineering, optimizing performance under high compliance conditions.

The Senior Machine Learning Engineer will design and build distributed systems for personalized content recommendations, ensuring deployment and optimization of ML platforms.

Lead end-to-end ML model development for safety-critical driving behaviors, design experiments, improve data quality, deploy models and metrics fleet-wide, and communicate results across product and engineering teams.

Develops machine learning software algorithms, supports data pipeline design, integrates model pipelines, monitors data quality, and engages with stakeholders for analytic solutions.

Own and improve production-scale ML models and systems for Spotify Home feed and Shortcuts. Build personalized recommendations and LLM-powered experiences, train and fine-tune LLMs, run A/B tests, optimize inference and cost, and improve ML platform, data pipelines, and production infrastructure at scale.

This role involves building and scaling machine learning systems for content understanding, improving quality, and collaborating with cross-functional teams to enhance user experience on Spotify.

The Machine Learning Engineer will develop and maintain scalable ML systems, collaborate across teams, and innovate solutions for real-time applications to enhance customer experiences.

Support a US government customer in Chantilly, VA by applying advanced analytics, statistical modeling, and machine learning to cyber-related data. Collaborate with cyber SMEs to build AI/ML models and analytics, work with cyber datasets (e.g., Shodan, Censys.IO), manage and process large volumes of unstructured text, and operate in Linux and Windows environments. Requires active TS/SCI with poly.

Lead design, build, deploy, and maintain production-grade AI/ML systems (LLMs, agentic architectures, classical ML). Provide hands-on technical leadership across architecture, model selection, implementation, testing, deployment, observability, and operational support while collaborating with data scientists, platform and security teams and mentoring engineers.

Lead and oversee enterprise ML and statistical modeling efforts, build and mentor data science teams, ensure model governance and compliance, collaborate cross-functionally to deliver scalable AI solutions that drive business outcomes, and advance research and adoption of cutting-edge ML methods.

Research and develop machine learning algorithms and software prototypes for sponsor problems. Perform data collection, modeling, fusion, visualization, statistical analysis, and present technical reports. Collaborate with staff, supervise junior personnel and students, and integrate ML prototypes into research and pre-production environments.

Lead design and delivery of end-to-end ML solutions for Wealth Management, build and deploy scalable models (recommender systems, GNNs, LLMs), run large experiments, validate and monitor production models, partner with cross-functional teams, and mentor data scientists to drive measurable business outcomes.