The engineer will design, experiment with, and implement Machine Learning models to address complex identity security challenges, taking ownership of research and prototyping efforts in areas like embeddings and representation learning. They will translate these prototypes into robust, production-ready systems while driving improvements in model accuracy and advocating for best practices in ML engineering and architecture.
Remote Machine Learning Jobs (628)
Search = machine learning
Job category = Software Development
The role involves building and productionizing core ML components, focusing on LLM and NLP models for search, summarization, and generative tasks by developing optimized pipelines and prompt strategies. Responsibilities also include designing scalable ML services and inference pipelines in Python, ensuring robust engineering discipline through testing and observability.
This role involves leading the design, development, and deployment of scalable machine learning and AI systems for enterprise clients, working at the intersection of ML, distributed systems, and production software engineering. The Lead ML Engineer will guide ML architecture decisions, mentor other engineers, and collaborate cross-functionally to deliver high-impact AI solutions.
The engineer will collaborate with researchers to execute research operations using existing infrastructure, focusing on characterizing neural network quality, failure modes, and edge cases based on research data. Key tasks include managing concurrent experiments, reviewing results, suggesting improvements, and writing technical reports detailing qualitative and quantitative outcomes.
The role involves researching and building deep learning systems for expressive speech generation and developing state-of-the-art generative systems for video and audio synthesis. As a senior member, the engineer will shape core infrastructure, set best practices, and mentor junior staff while delivering high-quality ML pipelines in production.
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The Staff Machine Learning Engineer will serve as the technical lead for an ML team, setting direction, designing, building, and scaling ML systems for personalization, relevance, ranking, search, or ad use cases. This role involves owning end-to-end architecture, driving design reviews, and mentoring engineers while remaining hands-on in development.
The Staff Machine Learning Engineer will architect and lead ML-driven map reconstruction systems using multi-modal sensor data at a national scale. Responsibilities include designing end-to-end pipelines for map reconstruction, defining technical strategy, and leading the adoption of state-of-the-art computer vision and ML techniques.
The mission involves developing, validating, and deploying Machine Learning models for performance and operational use cases, such as predictive analytics and decision support. This includes building data pipelines for structured and time-series data and implementing MLOps basics for monitoring deployed models.
The mission involves designing and executing applied research to validate the use of Machine Learning and Generative AI in real-world scenarios with prospects or clients. This is achieved by creating Proofs of Concept (POCs) to reduce technical uncertainty and establish foundations for productive and commercial initiatives.
The Principal Machine Learning Scientist will advance core computer vision model performance for warehouse inventory scanning across drone and MHE Vision platforms, owning the full ML lifecycle from research through production deployment and monitoring. This includes collaborating on model optimization for various inference targets and providing technical leadership and mentorship to the ML team.
The engineer will be responsible for designing, implementing, fine-tuning, improving, and debugging image AI models that power lifelike AI bots, focusing on generative image and machine vision services. This includes evaluating new research, developing and deploying new pipelines, monitoring production issues, and optimizing models for quality and latency.
The Director will define and own the Machine Learning strategy and technical roadmap, leading and growing the Machine Learning and FPT teams while establishing a culture of rigor and production-quality delivery. Key tasks include driving improvements to core computer vision models and building out MLOps infrastructure for model training, deployment, and monitoring.
The role involves hands-on machine learning development to contribute to designing, scaling, building, evaluating, integrating, shipping, and refining reward signals for recommendations. This includes leading collaborations across the Personalization team to integrate and A/B test mid-term signals in various recommendation systems.
![Smart Working Solutions is hiring for remote Machine Learning Engineer (Remote, Full-Time) [AS207]](/assets/08150176-369d-44db-a4a7-778571334971.png){kind=small}
Machine Learning Engineer (Remote, Full-Time) [AS207]
Smart Working Solutions
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15 Weeks Ago
Smart Working Solutions
The role involves architecting, building, and maintaining production-grade, low-latency ML services for ranking, recommendation, and forecasting use cases. This includes collaborating with cross-functional teams to bridge the gap between experimentation and production deployment.
Lead a team of machine learning engineers to research, design, and optimize AI models and services. Build relationships and drive alignment with stakeholders to enhance the Community Support experience.
The Senior Machine Learning Engineer will be responsible for designing, developing, and deploying robust machine learning models to solve real-world business challenges within the fintech domain. This includes collaborating cross-functionally, analyzing complex datasets, and contributing to the improvement of AI workflows and best practices.
The engineer will design, prototype, implement, evaluate, and optimize systems to generate highly accurate, low-latency sports datasets and predictions. They will also build, test, deploy, and maintain production systems while optimizing the data scientist's modeling workflow.
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Define and lead the technical vision for next-generation Agentic AI systems, architecting scalable, production-grade LLM systems that integrate reasoning, retrieval, planning, and tool use into intelligent workflows. Own the evaluation strategy for complex AI systems, proactively mitigating failure modes like hallucinations and prompt brittleness while defining measurable quality standards.
Develop and deliver end-to-end machine learning solutions, encompassing technical requirement definition, scalable system architecture, and implementing monitoring and maintenance workflows. Collaborate with cross-functional teams to build new ML products and lead the design and implementation of MLOps frameworks.
The role involves designing, building, deploying, monitoring, and optimizing production-ready Machine Learning services within a regulated healthcare environment. Key tasks include collaborating with scientists to deploy models, building serving infrastructure with monitoring, and implementing comprehensive testing for reliability and performance.
This role involves leading complex ML engineering projects, designing, developing, and debugging software solutions for customer-facing and internal applications. Responsibilities also include deploying, scaling, and maintaining machine learning models in production environments while developing scalable ML architecture and pipelines.
The Senior Machine Learning Engineer will design, develop, and deploy robust machine learning models to address real-world business challenges within the fintech sector. This role involves collaborating with cross-functional teams to translate requirements into scalable ML solutions and contributing to the improvement of AI workflows and best practices.
Engagements will involve developing machine learning models, integrating applied AI solutions into existing systems, and optimizing ML pipelines for clients. Responsibilities also include working on applied generative AI (GenAI) use cases.
The engineer will design and build simulation environments to model CTV auction mechanics, inventory supply, and advertiser competition, while also developing counterfactual frameworks for offline evaluation of bidding strategies. They will build AI agents for strategy exploration and use LLMs/generative AI to accelerate internal ML workflows like synthetic data generation and rapid prototyping.
The role involves developing and maintaining the ML training platform and the bidding infrastructure used to evaluate ML models in production. Responsibilities also include identifying performance bottlenecks, optimizing critical system parts, ensuring scalability, and creating performance tests for new components.
The role involves leading the design and development of computer vision systems for biometrics, including rigorous fairness analysis and benchmarking of models. Responsibilities also include owning end-to-end ML pipelines from data ingestion to deployment and optimizing models for low-latency inference.
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Machine Learning Engineer - ML Training Platform
Pluralis Research
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16 Weeks Ago
Pluralis Research
The role involves designing and implementing large-scale distributed training systems optimized for low-bandwidth, high-latency conditions, focusing on model parallelism and communication overhead reduction. Responsibilities also include architecting resilient training systems capable of handling node failures and designing decentralized coordination peer-to-peer topologies.
The role involves designing and shipping productionized Machine Learning pipelines, from data ingestion and modeling through deployment, monitoring, and iteration for core product areas like personalization, search, and recommendations. Key tasks include building ML-powered APIs and owning the full lifecycle of these systems while ensuring scalability and reliability.
The primary mission involves designing and optimizing large-scale pre-training systems to power generative AI models. This includes building scalable pre-training pipelines, implementing distributed training strategies across hardware, and developing monitoring systems for reliability.
Staff Machine Learning Engineer - Recommendations, Quora (Remote)
Quora
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Full Time
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17 Weeks Ago
Quora
The role involves improving existing machine learning systems using core coding and ML knowledge, and identifying new opportunities to apply machine learning across the Quora product. Responsibilities also include taking end-to-end ownership of ML systems, from data pipelines to production integration.
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