Top Remote Machine Learning Engineer Jobs

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

As an Open-Source Machine Learning Engineer at Hugging Face, you'll improve ML libraries, work with the community, and contribute to open-source projects.

Design and implement scalable ML solutions for customer experience problems. Analyze customer data, research and deploy ML/NLP models, automate model development and validation, and produce performance analyses.

Owns quality analysis for annotated computer vision and machine learning data. Responsibilities include reviewing annotation queues, maintaining verdict taxonomies and guidelines, tracking performance by facility and equipment, detecting anomalies, conducting root cause analysis, reporting findings, improving annotation tools, and establishing quality processes for new programs. The role requires independent Python and SQL analysis, strong statistical reasoning, clear documentation, and collaboration with ML and engineering teams.

Design and develop scalable, high-performance data and API infrastructure for real-time processing. Mentor engineers and collaborate with teams to enhance AI model evaluations.

Leads architecture, implementation, and scaling of production ML and LLM systems for legal products. Builds RAG pipelines, agentic applications, hybrid search, APIs, microservices, data pipelines, and model-serving infrastructure. Establishes reusable AI frameworks, deployment and observability practices, and CI/CD standards while optimizing latency, reliability, scalability, and cost. Partners with data scientists to productionize models and provides technical leadership in system design and engineering excellence.

Lead development of ML components for medical image reconstruction: define training/evaluation pipelines and metrics, productionize models (inference performance, monitoring, safe fallbacks), collaborate on hybrid physics-ML algorithms, and build tooling for experimentation and rigorous verification.

Own end-to-end ML model lifecycle from EDA and feature engineering to production deployment and monitoring. Collaborate with cross-functional teams to identify modeling opportunities, evaluate performance and business impact, integrate new data sources, and ensure scalable, observable production systems.

The Machine Learning Engineer will develop and enhance ML-powered products focusing on models, metrics, pipelines, and collaboration with product teams.

As a Fraud ML Engineer, you will ensure authentic human data is processed by building detection systems using adversarial ML techniques and managing backend systems.

Join PatternAI's data team as a Machine Learning Engineer, building ML pipelines and managing MLOps infrastructure for model optimization.

As a Machine Learning Engineer, you will focus on user-behavioral modeling, collaborate with teams, and engage in data processing and model deployment.

As a Machine Learning Engineer at Yobi, you will design and optimize systems for real-time model serving and inference, ensuring performance and reliability.

Lead the design and development of machine learning solutions, overseeing the entire software development lifecycle and mentoring engineers. Drive innovation in Generative AI and high-performance systems.

Build and maintain MLOps pipelines, deploy and monitor ML models as scalable services, transform and integrate healthcare data (FHIR/HL7), ensure reproducible feature management, and uphold HIPAA/HITRUST security for production ML in clinical systems.

Develop and deliver production-ready 3D and camera-based perception ML models (BEV emphasis). Own model training, evaluation, debugging, and integration into autonomy systems. Improve training pipelines, tooling, and robustness; lead small technical initiatives and mentor junior ML engineers while collaborating cross-functionally to deploy perception models at scale.

Optimize and deploy multi-modal models (transformers, diffusion, VLMs) to run on-device across mobile and desktop. Own export, quantization, kernel tuning, runtime integration, and profiling to meet strict latency, memory, and power budgets. Build packaging, benchmarking, and telemetry; partner with research to productionize models and mentor junior engineers.

Design, evaluate, and productionize generative AI/ML solutions (RAG, agents, embeddings, retrieval). Build evaluation frameworks for hallucination detection, benchmark LLMs, optimize prompts/models, create datasets, fine-tune models, and collaborate to deploy and monitor enterprise-scale GenAI systems.

Lead development of scalable AI/ML capabilities for Mail Intelligence: build LLM- and ML-based personalization, data pipelines, tools, and observability. Mentor engineers, collaborate cross-functionally, run A/B tests, implement feedback loops, and balance cost/quality/speed tradeoffs to deliver production-grade solutions.

Design, build, and operate core AI capabilities and reusable model integrations for an enterprise platform. Integrate foundation models into production, optimize performance and reliability, prototype new techniques, and support scalable backend services and distributed AI systems in collaboration with cross-functional teams.

Develop and deploy production machine learning models and sensor-fusion algorithms for autonomous-truck ego-motion estimation and localization. Build scalable PyTorch training workflows, analyze large vehicle datasets, and create robust C++ and Python production software. Define validation strategies, optimize performance under real-time constraints, make architecture decisions, and collaborate across autonomous-driving teams. Provide technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

Lead the development of device intelligence and fingerprinting systems by designing backend services, collaborating on integrations, and applying advanced machine learning algorithms to enhance security and combat fraud.

The Principal Machine Learning Engineer will fine-tune and optimize LLMs for various applications, develop AI products, and enhance ML infrastructure in collaboration with cross-functional teams.

The Staff Machine Learning Engineer will lead ML systems development for personalization and ranking, design architectures, and mentor team members, ensuring reliable operations at scale.