The role involves building intelligent systems by working with data and transforming machine learning models into practical, real-world solutions. This includes developing, implementing, and deploying these ML models.
Remote Machine Learning Engineer Jobs (529)
Search = Machine Learning Engineer
This role involves setting the technical strategy and driving the roadmap for ML platform capabilities, owning cross-team architecture decisions for the ML lifecycle and inference systems. Responsibilities include building core platform components like experiment tracking, model serving on Kubernetes, and establishing ML engineering standards.
The engineer will partner with research scientists to prototype and validate novel modeling ideas, scaling them through robust training systems for speech technologies, internal tooling, and innovative data strategies. Key duties involve architecting scalable training systems for STT/TTS models, designing internal UIs for ML workflows, and overseeing training tooling, job orchestration, and data storage.
The role involves analyzing user behavior data to identify personalization features, designing, training, and iterating on recommendation models using various approaches, and building end-to-end training and serving pipelines. Responsibilities also include defining evaluation metrics, running A/B tests, and owning the monitoring and observability of deployed models.
The engineer will design, build, and productionize domain-specialized AI capabilities focusing on advancing multi-step reasoning, structured synthesis, and tool-grounded intelligence for live customer environments. Responsibilities include developing scalable AI infrastructure and services, implementing monitoring and evaluation processes, and collaborating to transition prototypes into robust production systems.
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The Senior Machine Learning Engineer will design and implement scalable NLP architectures, including Transformers and Generative AI frameworks, to transform unstructured caregiver notes into production-grade AI models. Responsibilities also include building robust preprocessing pipelines, fine-tuning LLMs, and leading the adoption of MLOps best practices for CI/CD.
The Senior Machine Learning Engineer will own the entire ML lifecycle, from data and feature pipelines through training, deployment, monitoring, and retraining. This role involves translating business problems into reliable production ML systems that integrate with the platform and power product decisions.
This role involves serving as a technical architect to define the tooling strategy, evaluate and select AI frameworks, and orchestrate agent workflows leveraging LLMs and enterprise AI technologies. Key duties include developing AI-powered agents that integrate deeply into the product and establishing monitoring and feedback loops for continuous improvement.
The role involves designing and developing machine learning infrastructure, tooling, and models to enable teams to deliver world-class experiences and build internal platforms for incorporating AI features. You will also consult with teams on machine learning patterns, tradeoffs, and guide them through creating excellent end-to-end customer experiences.
Senior Machine Learning Engineer, Community Support Engineering
Airbnb
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Full Time
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18 Weeks Ago
Airbnb
The engineer will envision, champion, and support the development of novel Machine Learning systems, product integrations, and performance optimizations to solve real-world problems. They will also work cross-functionally to design and build efficient AI solutions for Airbnb Customer Service products.
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.
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 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.
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 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 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.
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![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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19 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.
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.
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 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.
Machine Learning Engineer - ML Training Platform
Pluralis Research
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20 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.
The engineer will be responsible for performing in-depth data analysis to understand security threats and designing, training, and evaluating machine learning algorithms to enhance threat detection models. Key tasks include building and productionizing ML models and developing distributed systems to analyze large volumes of URLs.
Sr. Applied Machine Learning Engineer - Search
Legion Intelligence
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Full Time
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21 Weeks Ago
Legion Intelligence
The role involves leading the development of machine learning models and data-driven algorithms for high-impact projects, collaborating with product and platform teams to own ML solutions end-to-end. Responsibilities also include building, maintaining enterprise products, supporting production systems, and communicating modeling decisions and tradeoffs.
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The role involves designing, training, testing, and iterating on diffusion models for generating 3D geological models conditioned on geophysical surveys and other physical observations. Responsibilities also include adapting the diffusion modeling approach for specific real-world clean energy projects in collaboration with project teams.
The Senior Machine Learning Engineer will be responsible for designing, building, and deploying advanced ML solutions to power business growth, leading complex projects, and mentoring peers. Key duties include developing and implementing sophisticated AI/ML models using various libraries and collaborating cross-functionally to ensure seamless integration and performance monitoring.
The role involves designing modular and scalable real-time data pipelines to manage large datasets and implementing custom Machine Learning algorithms within a low-latency environment. Responsibilities also include working on microservice architectures that handle the training, inference, and monitoring of thousands of ML models concurrently.
As a Senior Machine Learning Engineer, you will research, implement, and tune cutting-edge RL/ML models to achieve critical business outcomes. You will collaborate with the Algorithms team to build reliable, repeatable models that run in production with high quality.
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