306 Machine Learning Engineer jobs in United States available for remote work from home. Apply for positions such as Senior Machine Learning Engineer I - Ads Platform, Senior Machine Learning Engineer, Payments, Staff Machine Learning Engineer and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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Design and implement machine learning models to optimize ad ranking, relevance, and delivery across the Fetch platform. Collaborate with cross-functional teams to build scalable feature pipelines and maintain high-performance production inference systems.
You will architect and deploy large-scale AI and LLM-powered solutions to optimize payment transactions and enhance fraud detection. You will also collaborate with cross-functional teams to translate complex business requirements into high-performance, production-ready services.
You will own the personalization domain, setting the technical direction for models behind search, recommendations, and homepage feeds. You will also build foundational embeddings and ensure models are effectively deployed, monitored, and evaluated in production.
Design, train, and deploy reinforcement learning-based systems to solve high-impact decision-making problems. Manage the engineering complexity of training and evaluating policies at scale while ensuring stability and safety in production environments.
The Staff Machine Learning Engineer will design, build, and maintain scalable machine learning models and algorithms to support the mission of eliminating child sexual exploitation. They will lead technical initiatives, mentor engineering staff, and collaborate across product and legal teams to drive automated solutions.
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You will provide technical leadership to accelerate improvements in Trust & Safety models and infrastructure while collaborating with cross-functional teams. Additionally, you will architect innovative machine learning solutions and mentor engineering leaders to drive long-term technical strategy.
Design and build systems to improve the efficiency of machine learning training and inference workloads while developing tooling for profiling and monitoring model performance. Lead cross-functional initiatives to optimize resource utilization and drive technical strategy for platform scalability and cost efficiency.
The role involves designing and implementing end-to-end machine learning solutions for predictive maintenance, including data extraction, feature engineering, and model deployment on AWS. You will collaborate with cross-functional teams to build scalable pipelines and maintain production-ready systems for machinery health assessment.
You will drive the development and implementation of cutting-edge machine learning algorithms and sophisticated models to enhance Atlassian products. Additionally, you will design system architectures, conduct rigorous experimentation, and provide technical guidance to emerging machine learning engineers.
You will develop and optimize novel machine learning systems and agentic AI technologies to enhance Airbnb's customer service experience. This involves cross-functional collaboration to build scalable AI solutions from conceptual stages through to production.
Drive the development and implementation of advanced machine-learning algorithms for business-critical applications. Lead the end-to-end machine-learning development lifecycle from problem definition through production deployment.
Lead and design knowledge graph projects to build a personal working environment context layer for Rovo Chat and the Teamwork Graph CLI. Define inference pipelines, schemas, and evaluation frameworks while providing technical leadership across multiple product teams.
You will lead the Utility and multi-armed bandit workstream to optimize search ranking, reward design, and marketplace efficiency. You will also partner with cross-functional teams to scale machine learning models to production and mentor other engineers.
You will build and scale machine learning models and ranking algorithms to optimize job matching for millions of users in real-time. Additionally, you will develop simulation tools for offline evaluation and conduct A/B experiments to measure the impact of model improvements.
You will lead the design and implementation of Reddit's next-generation user understanding initiative, creating a unified representation layer to power personalization across the platform. This involves leveraging LLMs and foundation models to build scalable, high-fidelity user models while collaborating with cross-functional teams to drive measurable impact.
You will develop AI and machine learning solutions to automate security analysis and identify high-fidelity risks in customer data. This involves collaborating with threat-research teams to build data-driven components and deploying these solutions within a CI/CD framework.
The Senior Staff Machine Learning Engineer will develop high-fidelity risk identification solutions by leveraging AI, machine learning, and data engineering. They will collaborate with threat-research teams to build automated security analysis components and deploy these solutions within a production CI/CD framework.
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Develop and deploy machine learning models for autonomous truck behavior systems, including reinforcement learning and imitation learning. Collaborate with cross-functional teams to integrate these models into simulation and real-world autonomy stacks.
Build scalable AI proof-of-concepts that demonstrate a clear path from prototype to enterprise-scale solutions. Partner with cross-functional teams to translate business needs into practical AI systems using modern cloud-native technologies.
You will design and implement evaluation infrastructure for AI agents, including benchmark creation and LLM-as-judge systems. Additionally, you will conduct failure analysis and manage recurring evaluation workflows to support research teams.
The Sr. Machine Learning Engineer will collaborate with product teams to design, develop, and deploy scalable AI/ML algorithms and software solutions. Responsibilities include building data pipelines, monitoring production performance, and ensuring the resiliency and quality of enterprise machine learning systems.
Set technical strategy for the data graph, entity resolution, buying intent, and agent context, while designing and shipping new machine learning systems. Establish evaluation standards and data contracts, make model cost and capacity decisions, and guide technical reviews, mentoring, hiring, and collaboration with product and leadership.
Lead machine learning strategy for marketplace efficiency, defining objective and guardrail metrics and improving experimentation to balance outcomes for jobseekers, employers, and marketplace health. Partner with product, engineering, data science, and senior leadership to shape systems and long-term strategy, mentor colleagues, and contribute hands-on to code and data when needed.
You will lead the exploration and application of Large Language Models and Generative AI to enhance user experiences within the company's bot-building platform. Additionally, you will collaborate with cross-functional teams to develop, implement, and optimize high-performing ML systems.
Build foundational AI infrastructure for agent runtime, evaluation, quality measurement, model optimization, and fine-tuning. Develop evaluation and signal systems that identify quality gaps and agent failures, benchmark models on HubSpot workloads, and help teams improve product quality, cost, and model routing.
Build foundational AI infrastructure for agent runtime, evaluation, quality measurement, and model optimization across HubSpot. Develop evaluation and signal systems, benchmarks, model assessment, and repeatable fine-tuning and optimization capabilities to improve agent quality, cost, and scalability.
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The Principal Machine Learning Engineer will build and maintain ML systems while bridging the gap between research-level experimentation and production-grade infrastructure. They will also define the long-term search and discovery vision, leveraging large language models to drive business and revenue impact.
The engineer will design and deploy advanced machine learning models for ad targeting, identity resolution, and contextual signals. They will collaborate with cross-functional teams to build scalable, low-latency infrastructure that powers Netflix's advertising platform.
Lead the end-to-end delivery of machine learning initiatives and conversational AI architectures across the Block ecosystem. Partner with cross-functional leadership to define ML roadmaps and develop scalable tools that enhance support agent effectiveness.
The Machine Learning Engineer Co-op will develop, deploy, and optimize machine learning and generative AI models to enhance customer experiences and internal workflows. They will also build intelligent AI agents and maintain MLOps pipelines to streamline model evaluation and deployment.
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