Own the technical direction for pricing machine learning, defining the strategy and roadmap for the pricing engine. Build ML systems for price optimisation and solve imbalance problems while bridging modelling and production.
Remote Machine Learning Jobs (672)
Search = machine learning
Job category = Software Development
Senior Staff Machine Learning Engineer, GenAI Platform 11 APPLICANTS
Reddit
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Full Time
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18 Weeks Ago
Reddit
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.
Machine Learning Engineer - Distributed ML Systems
Pluralis Research
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Full Time
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18 Weeks Ago
Pluralis Research
Design and implement large-scale distributed training systems optimized for low-bandwidth, high-latency conditions. Build resilient training systems that can handle node failures and dynamic participant changes.
Machine Learning Engineer - ML Training Platform
Pluralis Research
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Full Time
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18 Weeks Ago
Pluralis Research
The role involves designing resource management systems for multi-cloud infrastructure and architecting fault-tolerant infrastructure for distributed machine learning. Additionally, the engineer will build systems that simulate real-world network conditions to ensure efficient data flow across consumer nodes.
This role involves designing and maintaining large-scale machine learning training systems capable of processing petabyte-scale multimodal robotics datasets across distributed GPU clusters. Key duties include optimizing training pipelines, refining neural network architectures, and collaborating with ML researchers to deploy high-performance autonomy models.
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The role involves leading the design, development, and deployment of production, multi-turn LLM-powered features, such as summarization tools and clinician-facing conversational agents. Responsibilities also include owning backend services in Python, defining technical direction for AI strategies, and establishing evaluation approaches for LLM outputs.
The Principal Machine Learning Engineer will develop large-scale, fault-tolerant multimodal agentic experiences and design multiple agent interaction modes, including Voice AI and Deep Research. They will also pioneer robust evaluation frameworks, tracing systems, and safety guardrails for highly-visible AI agents.
Software Engineer L4/L5 Training Platform, Machine Learning Platform
Netflix
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Full Time
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19 Weeks Ago
Netflix
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.
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.
Machine Learning Engineer with an Agentic Focus
High 5 Games
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Full Time
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19 Weeks Ago
High 5 Games
The role involves designing, developing, and deploying machine learning models and solutions, utilizing tools like LangGraph and MLflow for orchestration and lifecycle management. Responsibilities also include building scalable data pipelines using GCP services and implementing robust monitoring strategies for model performance and reliability.
The role involves envisioning, championing, and supporting the development of novel Machine Learning systems, product integrations, and performance optimizations to solve real-world problems. You will also work cross-functionally with product, design, and other engineering teams to build efficient AI solutions for Customer Service products.
Machine Learning Engineer / Data Scientist (Semi-Senior – Senior) (Remote)
GreenCode
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Full Time
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20 Weeks Ago
GreenCode
The role involves working on an intelligent matching project between doctors and pharmaceutical products, requiring technical evolution towards Generative AI and LLMs. Responsibilities include developing and maintaining data pipelines and applying classical Machine Learning techniques.
The Senior Machine Learning Engineer will drive the core ML models and underlying data and training pipelines, requiring autonomy in identifying, prioritizing, and executing workflows with peers and stakeholders. They will be responsible for owning their domain of expertise from project inception through delivery and beyond.
Senior Machine Learning Engineer – Perception & Embodied AI
General Motors
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Full Time
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20 Weeks Ago
General Motors
The engineer will own the end-to-end pipeline for safety-critical ML perception models, focusing on enabling the vehicle to accurately see, classify, and track environmental elements like road markings and vulnerable road users. Key tasks include designing and deploying deep learning models for 3D object detection, sensor fusion, and optimizing performance for real-time embedded hardware.
![Smart Working Solutions is hiring for remote Machine Learning Engineer (Mid/Senior) (Remote, Full-Time) [AS221]](/assets/08150176-369d-44db-a4a7-778571334971.png){kind=small}
Machine Learning Engineer (Mid/Senior) (Remote, Full-Time) [AS221]
Smart Working Solutions
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Full Time
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20 Weeks Ago
Smart Working Solutions
The Machine Learning Engineer will be responsible for driving the development and integration of AI capabilities by building end-to-end ML solutions, from data modeling through to system deployment. Key tasks include automating complex processes, translating business problems into scalable ML solutions, and promoting machine learning best practices across the organization.
The Senior Machine Learning Engineer will design, implement, and validate high-reliability, distributed platforms for machine learning, natural language processing, and LLMs. This role involves creating, debugging, and improving production models while building tools to translate data insights into scalable action.
Staff Machine Learning Engineer - Content and Contributor Intelligence (Remote - United States)
Yelp, Inc
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Full Time
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20 Weeks Ago
Yelp, Inc
The Staff ML Engineer will conduct end-to-end analyses, wrangle data using SQL or Python, and develop/deploy machine learning models for both batch and real-time services, focusing on text and visual understanding.
This role involves mentoring junior engineers, driving best practices in ML development and operations, and productionizing automated model pipelines within Python services.
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The role involves identifying customer problems and leading teams to research, architect, design, and operationalize AI/ML solutions across core areas like Search, Recommendations, and Ads. This includes defining technical strategy, driving cross-functional alignment, and leading mid-to-long-term research initiatives.
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 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.
Senior Machine Learning Engineer - Automatic Speech Recognition (ASR)
Cresta
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Full Time
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20 Weeks Ago
Cresta
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.
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.
The role involves developing novel Machine Learning and Deep Learning methods for medical image analysis, including segmentation, detection, and outcome prediction, while ensuring these methods are scientifically rigorous and clinically meaningful. Responsibilities also include publishing findings in top-tier venues and collaborating with clinicians to translate research into validated outputs for production engineering and regulatory submissions.
Software Engineer L4/L5, Model Serving Systems, Machine Learning Platform
Netflix
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Full Time
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20 Weeks Ago
Netflix
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.
Software Engineer L4/L5 - Data and Feature Infrastructure, Machine Learning Platform
Netflix
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Full Time
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20 Weeks Ago
Netflix
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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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 role involves conducting applied research in AI/ML to solve real-world problems in home design using various data types, and designing, implementing, and validating prototypes and MVP solutions for production features. This includes training and iterating on models, contributing to AI-driven features like computer vision systems, and collaborating with engineers to deploy research into production.
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 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.
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