You will implement, validate, and iterate on machine learning algorithms for weld perception tasks and build robust data pipelines for model training. Additionally, you will lead the development of advanced perception systems and define the end-to-end ML lifecycle from dataset design to fleet deployment.
Remote Machine Learning Engineer Jobs (529)
Search = Machine Learning Engineer
Staff / Principal Machine Learning Engineer, Serving - Switzerland
Inworld AI
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Full Time
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14 Weeks Ago
Inworld AI
You will be responsible for optimizing multimodal model inference and managing high-performance serving systems at scale. This involves taking models from research to production, ensuring reliability, and handling thousands of concurrent connections.
Senior / Lead Machine Learning Engineer, Serving - Serbia
Inworld AI
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Full Time
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14 Weeks Ago
Inworld AI
You will be responsible for optimizing multimodal model inference and ensuring reliable, high-performance serving at scale. This involves taking models from research to production, containerizing them, and managing distributed systems to handle thousands of concurrent queries.
Senior Machine Learning Engineer - Learned Planning/Reinforcement Learning
Torc Robotics
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Full Time
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14 Weeks Ago
Torc Robotics
You will design, develop, and deploy learned behavior models for autonomous trucks using reinforcement learning and imitation learning. This role involves owning end-to-end model development, writing production-quality code, and collaborating with cross-functional teams to improve model performance and reliability.
Sr. Distinguished Machine Learning Engineer (Remote-Eligible)
Capital One
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Full Time
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14 Weeks Ago
Capital One
Define and drive the technical strategy for a personalization platform that powers real-time, multi-channel customer experiences. Architect and maintain robust ML infrastructure, pipelines, and low-latency systems to support end-to-end model development and deployment.
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You will lead engineering initiatives to build scalable, reusable infrastructure that accelerates the machine learning modeling lifecycle. This involves collaborating with cross-functional teams to design systems that improve predictive accuracy and operational efficiency.
You will design and develop machine learning infrastructure, models, and internal platforms to enable teams to build AI-powered features at scale. Additionally, you will consult with cross-functional teams to guide them through the data lifecycle and implement scalable, resilient services.
The Principal Machine Learning Engineer will oversee the design, development, and deployment of machine learning models for Sezzle's financial platform. Responsibilities include leading the creation of scalable ML solutions, collaborating with product teams, and mentoring team members.
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.
Senior Staff Machine Learning Engineer, GenAI Platform 11 APPLICANTS
Reddit
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Full Time
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15 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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15 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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15 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.
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.
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.
The role involves designing and implementing state-of-the-art recommender systems to boost product discovery and customer engagement across platforms. Responsibilities include building scalable machine learning pipelines using tools like Databricks and Spark, and applying advanced models such as Wide & Deep and Transformer-based architectures.
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The engineer will be responsible for building and productionizing feedback loops to continuously improve the AI agent's performance over time. This includes owning signal pipelines end-to-end, building evaluation infrastructure, and designing appropriate learning approaches.
Machine Learning Engineer with an Agentic Focus
High 5 Games
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Full Time
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16 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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16 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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16 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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16 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 Staff Machine Learning Engineer will design, implement, and optimize various ML models, translating research prototypes into scalable, production-grade systems. This role involves leading technical efforts to improve model accuracy, enhancing ML infrastructure and pipelines, and setting technical direction for ML engineering best practices.
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.
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Staff Machine Learning Engineer - Content and Contributor Intelligence (Remote - United States)
Yelp, Inc
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Full Time
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17 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.
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 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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17 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.
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