501 Machine Learning Engineer jobs available for remote work from home. Apply for positions such as Senior Machine Learning System Engineer, Matterport - Lead Machine Learning R&D Engineer, Senior Machine Learning Operations Engineer and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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Design and deploy scalable machine learning systems and retrieval pipelines to power search experiences across Atlassian products. Develop production-grade ML models and retrieval systems for agentic and RAG use cases while ensuring operational excellence and cost efficiency.
Lead the research, design, and implementation of cutting-edge ML algorithms to enhance spatial computing and digital twin creation. Translate research prototypes into production-ready solutions and contribute to the strategic ML roadmap and intellectual property.
Architect and maintain the machine learning production lifecycle by designing scalable infrastructure and automation pipelines. Collaborate cross-functionally to deploy, monitor, and optimize AI models to ensure high performance and security.
Design and build scalable data infrastructure for large-scale feature and training set computation and storage. Develop frameworks for batch and real-time features while partnering with ML engineers to integrate workflows into production systems.
Design and build scalable data infrastructure for large-scale feature and training set computation and storage. Develop frameworks for batch and real-time features while partnering with ML engineers to integrate workflows into production systems.
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Build and operate a real-time inference service and model deployment infrastructure to support risk decisioning. Establish model observability, drift detection, and experimentation capabilities like champion/challenger routing.
Design and implement scalable, high-performance ML ranking systems to power personalized feeds and search at Reddit scale. Collaborate with product and infrastructure teams to translate requirements into robust ML and Generative AI production systems.
Lead the technical design and launch of agent harnesses for Rovo Chat to improve quality, reliability, and latency. Collaborate with cross-functional teams to realize the transformative potential of AI across Atlassian's offerings.
Design and scale automated CI/CD and continuous training pipelines to reliably deploy and monitor machine learning models. Collaborate with cross-functional teams to optimize cloud-based ML infrastructure and ensure robust data lineage and governance.
Lead the development of scalable AI/ML infrastructure for multi-billion parameter foundation models used in autonomous vehicle simulations. Collaborate with global teams to improve simulation realism and mentor junior engineers on architectural decisions.
Own the full lifecycle of ML model development, focusing on forecasting and predictive modeling for grid capacity. Collaborate cross-functionally to translate business objectives into production-grade data analytics and operational workflows.
Design and maintain scalable infrastructure for serving machine learning models in real-time within a high-scale advertising ecosystem. Partner with ML engineers to productionize models and optimize latency, throughput, and reliability.
Design and maintain scalable infrastructure for serving machine learning models in real-time within a high-QPS advertising ecosystem. Collaborate with ML engineers to productionize models and optimize inference performance, latency, and cost-efficiency.
Design and maintain scalable infrastructure for serving machine learning models in real-time within a high-scale advertising ecosystem. Partner with ML engineers to productionize models and optimize inference performance, latency, and cost-efficiency.
Design and deploy full-stack machine learning algorithms for e-commerce, logistics, and consumer trend use cases. Build frameworks and tools to multiply the effectiveness of the data science team while providing technical leadership.
Lead the transition of machine learning models from research prototypes into scalable, high-performance production systems on AWS. Design and deploy custom ML solutions while optimizing inference throughput and latency using tools like ONNX and Flash Attention.
Develop novel machine learning methods and contribute to the team's research agenda to improve computer vision applications. Translate research findings into scalable production systems while identifying areas for innovation.
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The role involves leading the development of a platform for large-scale ML models, designing end-to-end model lifecycle patterns (MLOps) to increase ML engineer development velocity. This includes zero-to-one development of a graph ML codebase and optimizing model training efficiency in a distributed environment.
The Staff ML Infrastructure Engineer will lead the development of a platform for large-scale ML models, designing end-to-end MLOps patterns for data preparation, model management, and experiment tracking. This role involves zero-to-one development of a graph ML codebase and platform, collaborating on performance tuning, and architecting pipelines for massive graph data structures.
The MLOps Engineer will design, deploy, and maintain scalable and ethical autonomous AI systems. Key tasks include model deployment, cloud infrastructure management, automation of workflows, and ensuring compliance with security and ethical standards.
The role involves conducting research in machine learning and computer vision to develop algorithms for spatial understanding and digital twin creation. Additionally, the engineer will collaborate with teams to translate research into production-ready solutions.
Train state of the art models and research cutting edge algorithms for enterprise customers. Build agents that leverage proprietary algorithms to optimize datasets.
You will build synthetic data pipelines to generate enterprise environments for reinforcement learning post-training and create agents to convert production traces into actionable insights. Additionally, you will contribute to the agent building product and train state-of-the-art models for enterprise deployment.
You will develop and maintain model validation and security scanning pipelines to ensure quality and safety before deployment. Additionally, you will implement optimization techniques like quantization and pruning to improve inference latency and model performance.
Design and deploy intelligent AI agents and machine learning models using frameworks like LangChain and TensorFlow. Integrate these AI capabilities into full-stack web applications and manage scalable data pipelines on AWS.
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The engineer will design and build the platform supporting large-scale machine learning model training, fine-tuning, transformation, and evaluation workflows across the entire company. This includes co-designing and optimizing systems for scalability and cost-effectiveness, while creating easy-to-use APIs for ML practitioners and non-experts.
Develop and deploy machine learning models and build backend systems using Python. Integrate AI solutions with full-stack applications and develop REST APIs.
Engineers will develop and expand compute infrastructure to support growing AI needs, enabling ML application in new business areas and driving innovation across Netflix. This role involves powering business-critical models and taking ML/AI initiatives to the next level through cross-functional partnerships.
The role involves designing and building a next-generation ML data and feature platform to significantly improve ML practitioner productivity, focusing on defining, testing, computing, storing, and serving features for training and inference. Responsibilities include developing near-real-time feature computation engines, managing serving infrastructure, and building centralized feature and embedding stores for sharing across ML domains.
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