Senior Machine Learning Engineer
Design, develop, and deploy machine learning models to enhance risk and fraud detection systems. Lead technical efforts in the Risk/Fraud area and partner with operations to respond to evolving events.
306 Machine Learning Engineer jobs in United States available for remote work from home. Apply for positions such as Senior Machine Learning Engineer, Machine Learning Engineer, Simulation Evaluation, Member of Technical Staff (Machine Learning Engineer) and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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Design, develop, and deploy machine learning models to enhance risk and fraud detection systems. Lead technical efforts in the Risk/Fraud area and partner with operations to respond to evolving events.
Lead the design and deployment of evaluation approaches to assess the realism of multimodal world models and generative systems. Architect scalable machine learning pipelines and discriminator models to improve simulator realism for autonomous driving.
Translate cutting-edge research into production-ready machine learning systems for image and video processing. Own the full ML lifecycle from experimentation and training to deployment and scaling in cloud environments.
Lead complex data science projects to improve manufacturing processes and develop machine learning workflows from analysis to embedded deployment. Define data ingestion and governance standards while providing technical leadership and mentoring to team members.
Architect and scale enterprise-grade machine learning infrastructure and deployment pipelines to support clinical initiatives. Drive MLOps strategy, mentor engineering teams, and ensure the reliability and compliance of mission-critical ML services.
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Design and optimize large-scale continual pre-training pipelines for VLM foundation models. Conduct research on integrating multimodal data streams and develop metrics to evaluate model performance in autonomous driving contexts.
The role involves identifying high-value opportunities from data and building practical ML or heuristic solutions to improve developer workflows. You will own the end-to-end execution from data exploration and modeling to backend integration and productization.
Lead the design, development, and deployment of sophisticated ML and GenAI models to optimize clinical trial workflows and patient engagement. Collaborate with cross-functional teams to build scalable production-level pipelines and mentor junior engineers on ML best practices.
Partner with PhD researchers to design and implement production-quality machine learning models for quantitative trading strategies. Develop and maintain complex data pipelines and extensible tools to accelerate the model development lifecycle.
Architect and operate data and training pipelines across cloud and cluster environments to improve the autonomous driving stack. Build and maintain distributed training, orchestration tooling, and metadata stores for evaluation workflows.
Own the end-to-end lifecycle of production ML models, including training, optimization, deployment, and monitoring. Collaborate with research scientists to integrate new architectures into scalable systems serving millions of users.
Lead the deployment and management of machine learning models and scalable pipelines across production systems. Collaborate with cross-functional teams to integrate ML models into applications and optimize inference for mobile and embedded devices.
Lead the research and development of pCTR and conversion prediction models to improve ads relevance and calibration. Design debiasing techniques and contribute to next-generation foundation models and generative retrieval systems.
Responsible for fine-tuning and optimizing state-of-the-art LLMs for diverse use cases and high-performance deployment on Airbnb's ML infrastructure. You will design AI products like AI Assistants and autonomous agents while creating a multi-year technical roadmap.
Design and manage robust data pipelines and develop novel ML models for Large Quantitative Models and agentic frameworks. Collaborate with cross-functional teams to translate business objectives into actionable ML development and production roadmaps.
Design and optimize core bidding algorithms and auction mechanisms to maximize advertiser returns. Architect scalable bid landscape forecasting and analyze marketplace dynamics to drive algorithmic improvements.
Develop pipelines for post-training tasks including fine-tuning, evaluation, and model compression. Implement scalable systems for model deployment and optimization while collaborating with researchers to validate results in production.
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Own the end-to-end ML/AI lifecycle from defining success metrics and prototyping to deploying production-ready models. Collaborate with engineering and product teams to automate complex tax and accounting workflows using LLMs and traditional ML.
Design and deploy ML models for document classification, entity extraction, and summarization of complex financial documents. Build scalable production-ready ML services and contribute to the MLOps stack including CI/CD and evaluation frameworks.
You will lead the design, development, and productionization of machine learning models to drive user acquisition, activation, and retention. You will own the full model lifecycle and collaborate with cross-functional teams to identify high-impact growth opportunities.
You will set the technical direction for the robot learning team, designing architectures for perception, reasoning, and action generation. Additionally, you will mentor junior engineers and drive the deployment strategy for complex loco-manipulation systems.
You will lead the technical architecture and productionization of scalable machine learning systems to support real-time inference and platform integrity. This role involves partnering with data science teams to build robust data pipelines, feature stores, and automated MLOps workflows.
You will lead the design and development of AI-powered agentic systems to drive sustainable growth and personalize customer experiences at scale. This involves collaborating with cross-functional teams to productionize machine learning models and mentor engineers in building resilient, high-performing AI infrastructure.
You will lead technical execution and design AI-powered solutions while serving as a customer-facing leader for mission-critical projects. Your role involves orchestrating complex data pipelines, mentoring cross-functional teams, and translating customer requirements into scalable technical architectures.
The Senior Machine Learning Engineer will own the end-to-end lifecycle of high-impact ML projects, from offline experimentation to production deployment. They will also collaborate with cross-functional teams to enhance fraud detection effectiveness and scale infrastructure.
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.
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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.
As a Senior Staff Software Engineer, you will define and lead the vision for Reddit’s large-scale GenAI Platform, shaping the strategy and architecture for generative AI products. You will also drive MLOps and LLMOps standards and partner across teams to align platform investments with company priorities.
The Staff Machine Learning Engineer will architect, build, and deploy large-scale ML systems across recommendations, search, messaging, and content understanding, leading high-impact initiatives from ideation through production and iteration. This role involves designing and improving ranking models, building next-gen AI-powered experiences including LLM-integrated systems, and establishing best practices for ML development and responsible AI.
The role involves designing, implementing, and maintaining evaluation frameworks to measure model performance across ASR and NLP systems, while leading ASR quality improvement efforts through error analysis and metric definition. Responsibilities also include developing, training, and deploying machine learning models for speech recognition and downstream tasks, and partnering with research to deploy improvements.
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