Design, train, and deploy machine learning models across multimodal domains for safety, security, and intelligence applications. Build agentic systems and evaluation pipelines to measure model reliability and safety in production environments.
Remote Machine Learning Engineer Jobs (455)
Search = Machine Learning Engineer
The role focuses on productionalizing machine learning models for rich media experiences and building scalable ML systems. Responsibilities include owning data pipelines, establishing CI/CD for model deployment, and collaborating with scientists to optimize model performance.
Design and operate AWS-based infrastructure for data extraction, transformation, and loading to deliver predictive insights. Build and optimize complex data lakes and ML models to support customer data architecture and business needs.
The role involves researching and implementing novel ML-based solutions for Cyber Defense and developing agentic solutions for Cyber Incident Response. The engineer will solve complex security architecture challenges with a focus on real-time performance and scalability.
Design and optimize AI models for image search and online content monitoring using computer vision and NLP. Build scalable data pipelines and continuously monitor production model performance to improve accuracy.
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Design and optimize advanced algorithms for workflow automation, computer vision, and signal processing. Collaborate with cross-functional teams to integrate these algorithms into the software ecosystem and optimize them for edge devices and GPUs.
Design, build, and productionize machine learning systems to solve supply and demand challenges within logistics. Develop ML pipelines for predicting ETAs and optimizing pricing to ensure efficient delivery operations.
Develop and scale high-performance ML prediction models for real-time adtech environments, owning the full pipeline from raw data to online serving. Design and refine complex bidding algorithms and conduct offline simulations and online A/B tests to optimize business metrics.
Design, develop, and deploy Machine Learning models and ML pipelines for a healthcare client. Focus on optimizing models for document classification, segmentation, and data extraction in production environments.
Design and optimize machine learning models for medical image analysis to improve prostate cancer diagnosis and treatment. Build scalable data pipelines and integrate AI models into clinical software solutions while ensuring regulatory compliance.
Lead the design and optimization of scalable cloud-based data pipelines and engineering solutions. Implement AI-driven initiatives using LLM and RAG frameworks to enhance intelligent data solutions.
Build and maintain the ML infrastructure for ranking models to personalize the customer shopping experience. Collaborate with cross-disciplinary teams to implement end-to-end ML solutions that impact business KPIs.
Design, train, and deploy machine learning models at the edge to detect anomalies and block sophisticated cyber attacks in real time. Build robust data pipelines to automate defense mechanisms and monitor model performance against evolving threats.
Build and maintain ML pipelines for data ingestion, training, and deployment within an AWS environment. Collaborate with global teams to adapt existing AI infrastructure for US-specific insurance use cases.
Machine Learning Engineer
Keysight Technologies, Inc.
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Full Time
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3 Weeks Ago
Keysight Technologies, Inc.
Lead the design, development, and deployment of scalable machine learning models to drive business decisions across domains like Sales, Finance, and Supply Chain. Collaborate with cross-functional teams to build production-ready ML pipelines and ensure robust model governance and monitoring.
Define model development methodologies and best practices while managing the full ML lifecycle. Develop tools and pipelines to support the productionalization of ML-driven microservices.
Own the full machine learning lifecycle from problem discovery and design to deployment and monitoring of ML-powered features. Collaborate with cross-functional teams to implement models that improve customer experience and business growth.
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Spearhead machine learning initiatives to generate and visualize home décor items in real-time. Collaborate with design and development teams to refine models and enhance the realism of product rendering.
Develop machine learning models to automate the generation of scientifically accurate, editable visuals from research inputs. Implement natural language-based editing tools to enable intuitive figure creation while maintaining scientific integrity.
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.
Develop and implement AI agents and automation to optimize engineering workflows and customer-facing delivery processes. Collaborate with cross-functional teams to integrate AI solutions into existing infrastructure while ensuring performance, safety, and reliability.
Machine Learning Engineer 23 APPLICANTS
Blue Orange Digital
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Full Time
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9 Weeks Ago
Blue Orange Digital
The role involves prototyping new modeling methodologies and building data pipelines for global AI products. You will also provide client support for complex modeling tasks and perform quality assurance on new product features.
The engineer will contribute to the design, build, evaluation, shipping, and refinement of systems aimed at improving promotional performance through hands-on ML development. This includes collaborating with multidisciplinary teams to optimize models for production use cases and influencing technical design and infrastructure decisions.
This role involves partnering with stakeholders to define requirements and translate business problems into measurable ML problem statements, while designing, implementing, and maintaining scalable, enterprise-grade ML solutions in production environments. Responsibilities also include building reproducible ML workflows, implementing monitoring frameworks for continuous improvement, and owning operational excellence like SLAs and incident response.
This role involves partnering with stakeholders to define ML problem statements and then designing, implementing, and maintaining scalable, enterprise-grade ML solutions in production environments. Responsibilities also include building reproducible ML workflows, implementing monitoring frameworks, and owning operational excellence like SLAs and incident response.
The engineer will own the entire machine learning lifecycle, from initial exploration and model development through to production deployment and ongoing iteration. Key tasks include expanding the model portfolio, driving the ML engineering process to production, and building rigorous evaluation frameworks for all models.
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The core responsibilities involve joining the team to work on machine learning engineering tasks. Further details about specific duties are not provided in the description.
The engineer will collaborate with the Head of AI and product teams to develop and deploy AI-driven security features, focusing on designing, training, and integrating machine learning models for detecting malicious traffic and bots.
The engineer will own the end-to-end Machine Learning lifecycle, including researching, designing, and implementing novel ML solutions for complex business problems. Key tasks involve rapid prototyping, rigorous experiment tracking, deploying models using CI/CD, and maintaining performance monitoring systems.
The Machine Learning Engineer will build statistical models and production systems focused on balancing advertiser performance with overall business goals, while also tuning optimization parameters and modeling dynamic environments. Responsibilities include designing and running experiments to validate theories about the mobile ad tech economy and developing applications related to budget retention, margin allocation, and bidding innovations.
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