Top Senior Machine Learning Jobs

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.

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.

Lead the design and delivery of complex ML systems, architect integrations, and set high standards for engineering practices while collaborating with teams to develop robust solutions and mentor other engineers.

The Staff Machine Learning Engineer will design and deliver complex ML systems, lead integration efforts, build scalable solutions, and optimize model deployments, collaborating with various teams and mentoring peers.

Join a talent network connecting Principal AI/ML Engineers with VC-backed startups. Responsibilities include architecting and deploying ML/DL models, researching state-of-the-art AI (LLMs, transformers, RL), building scalable training and inference pipelines, leading AI strategy and technical teams, ensuring robust MLOps, optimizing models for production, and collaborating across engineering, data, and product.

Join a talent network connecting Principal AI/ML Engineers with VC-backed startups. Responsibilities include architecting and deploying production ML/DL models, researching state-of-the-art methods (LLMs, transformers, RL), building scalable training/inference pipelines, implementing robust MLOps, optimizing model performance, and leading technical strategy and teams.

Design, develop, and deploy machine learning models and data pipelines for naval applications. Build and optimize distributed, cloud-native analytics and event-streaming systems, work with multiple data formats and GIS tools, containerize and monitor deployments, apply DoD security practices, and collaborate with multidisciplinary teams to deliver mission-ready AI solutions in shipboard and operational Navy environments.

Design, build, and deploy production ML models and scalable pipelines to improve recommendations, ranking, and personalization. Own end-to-end workflows from data exploration and feature engineering to training, evaluation, experimentation (A/B tests), and production deployment while applying responsible AI practices.

Lead and grow a small ML team to turn Evvy's vaginal microbiome dataset into validated, product-ready diagnostic and prognostic models. Drive technical quality, roadmap planning, cross-functional translation, model validation and deployment readiness, hiring, and day-to-day team management.

Lead design and productionization of scalable ML/LLM systems for legal products. Build LLM applications (RAG, agents), hybrid search, model serving, data pipelines, APIs, and cloud-native infrastructure. Optimize for latency, reliability, and cost; establish deployment/observability best practices; partner with data scientists and provide technical leadership and coaching.