You will build and improve core machine learning components across data, training, evaluation, and inference pipelines. Additionally, you will debug production model issues and collaborate with product teams to ensure reliability and performance.
Remote Machine Learning Jobs (628)
Search = machine learning
Job category = Software Development
You will own critical machine learning subsystems end-to-end, from data preparation and training to production deployment and iteration. You will collaborate with research and engineering teams to build reliable, scalable AI systems that solve complex, long-horizon tasks.
You will own the end-to-end execution of ML systems, including data pipelines, training workflows, and inference architecture. You will also collaborate with application engineering to integrate these models into production products while ensuring reliability and performance.
You will own the end-to-end execution of ML systems, including data pipelines, training workflows, and inference architecture. You are responsible for translating research into production-ready solutions while ensuring system reliability, performance, and scalability.
You will define the research direction for the company's core AI intelligence, focusing on reasoning, memory, and planning. You will also establish evaluation frameworks to ensure real-world usefulness, safety, and robustness of the AI system.
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Senior Analyst, Predictive Analytics and Machine Learning
Credit Acceptance
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Full Time
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10 Weeks Ago
Credit Acceptance
The Senior Analyst will design, develop, and maintain complex statistical and machine learning models to support business decisions in pricing and credit risk. They will also monitor production models, perform exploratory data analysis, and ensure data quality for high-volume decision-making.
You will own the end-to-end deployment of machine learning models, including data validation, model selection, and performance monitoring. You will also collaborate with clients to understand their business needs and ensure the forecasting system delivers actionable results.
The architect will design and maintain hybrid game and machine learning systems while serving as the primary integration lead. They will also support programming teams, establish quality guidelines, and disseminate machine learning knowledge across the organization.
Manage and mentor a team of machine learning and data engineers while overseeing the end-to-end execution of large-scale ML solutions. Partner with product managers and architects to define technical roadmaps and ensure operational excellence in fraud-fighting and risk prediction systems.
Architect and evolve end-to-end machine learning and real-time data platforms to ensure reliability, security, and cost efficiency. Lead cross-functional engineering efforts and mentor staff while implementing MLOps best practices for high-volume workloads.
Design, develop, and deploy machine learning solutions to solve complex fulfillment and marketplace challenges. Collaborate with cross-functional teams to integrate models that improve operational efficiency and the shopper experience.
You will build and productionize forecasting models to project subscriber growth, engagement, and revenue across global markets. Additionally, you will partner with cross-functional teams to translate model outputs into actionable business recommendations for pricing and strategy.
You will implement, validate, and iterate on machine learning algorithms for weld perception tasks and build robust data pipelines for model training. Additionally, you will lead the development of advanced perception systems and define the end-to-end ML lifecycle from dataset design to fleet deployment.
You will design, develop, and optimize AI systems and ML pipelines to automate complex healthcare credentialing and verification processes. You will also lead ML initiatives end-to-end, mentor engineering team members, and drive architectural decisions for scalable AI solutions.
You will scope, design, and deploy scalable machine learning systems while partnering with product and engineering teams to execute the AI/ML roadmap. Additionally, you will build robust data pipelines and drive high engineering standards through mentoring and code reviews.
Design and implement machine learning and optimization algorithms to enhance ad quality and performance. Collaborate with product teams to define optimization objectives and communicate technical results to stakeholders.
Sr. Director, Machine Learning Engineering (Remote-Eligible)
Capital One
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Full Time
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10 Weeks Ago
Capital One
Lead and scale a high-performing engineering organization responsible for the Personalization Platform and real-time decisioning systems. Define technical strategy, manage cross-functional partnerships, and drive the adoption of state-of-the-art AI and LLM optimization techniques.
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Staff / Principal Machine Learning Engineer, Serving - Switzerland
Inworld AI
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Full Time
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10 Weeks Ago
Inworld AI
You will be responsible for optimizing multimodal model inference and managing high-performance serving systems at scale. This involves taking models from research to production, ensuring reliability, and handling thousands of concurrent connections.
Senior / Lead Machine Learning Engineer, Serving - Serbia
Inworld AI
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Full Time
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10 Weeks Ago
Inworld AI
You will be responsible for optimizing multimodal model inference and ensuring reliable, high-performance serving at scale. This involves taking models from research to production, containerizing them, and managing distributed systems to handle thousands of concurrent queries.
You will scope, design, and deploy scalable machine learning systems while partnering with product and engineering teams to execute the AI/ML roadmap. Additionally, you will build robust data pipelines and drive high engineering standards through mentoring and code reviews.
You will develop, train, and deploy machine learning models using large-scale, multi-modal healthcare data to generate actionable health insights. You will also own end-to-end ML workflows, including prototyping, feature generation, validation, and monitoring in a production environment.
The Machine Learning Engineer will design, build, and deploy production-grade ML models using healthcare and pharmacy data to improve patient outcomes and operational efficiency. They will also implement MLOps best practices and ensure all systems comply with healthcare regulations like HIPAA.
Senior Machine Learning Engineer - Learned Planning/Reinforcement Learning
Torc Robotics
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Full Time
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10 Weeks Ago
Torc Robotics
You will design, develop, and deploy learned behavior models for autonomous trucks using reinforcement learning and imitation learning. This role involves owning end-to-end model development, writing production-quality code, and collaborating with cross-functional teams to improve model performance and reliability.
Sr. Distinguished Machine Learning Engineer (Remote-Eligible)
Capital One
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Full Time
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10 Weeks Ago
Capital One
Define and drive the technical strategy for a personalization platform that powers real-time, multi-channel customer experiences. Architect and maintain robust ML infrastructure, pipelines, and low-latency systems to support end-to-end model development and deployment.
You will lead engineering initiatives to build scalable, reusable infrastructure that accelerates the machine learning modeling lifecycle. This involves collaborating with cross-functional teams to design systems that improve predictive accuracy and operational efficiency.
You will design and develop machine learning infrastructure, models, and internal platforms to enable teams to build AI-powered features at scale. Additionally, you will consult with cross-functional teams to guide them through the data lifecycle and implement scalable, resilient services.
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The Machine Learning Scientist will apply modeling and machine learning techniques to business problems, focusing on identity modeling for the ads member base. They will also serve as a strategic thought partner to product managers and business stakeholders.
The Principal Machine Learning Engineer will oversee the design, development, and deployment of machine learning models for Sezzle's financial platform. Responsibilities include leading the creation of scalable ML solutions, collaborating with product teams, and mentoring team members.
Machine Learning Engineer II - Behavioral Security Products 13 APPLICANTS
Abnormal
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Full Time
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11 Weeks Ago
Abnormal
The role involves developing machine learning algorithms for behavioral modeling and cybersecurity attack detection, as well as collaborating with cross-functional teams to implement effective solutions. Additionally, the engineer will monitor and improve production models and participate in code reviews.
Own the technical direction for pricing machine learning, defining the strategy and roadmap for the pricing engine. Build ML systems for price optimisation and solve imbalance problems while bridging modelling and production.
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