Top Remote Machine Learning Engineer Jobs

Build and operate model validation, quantization, and safety systems for production ML. Develop pipelines for security scanning, optimization (quantization/pruning), routing, prompt management, and evaluation frameworks measuring accuracy, performance, and safety across model lifecycles.

Develop, deploy, and support secure, scalable production software and AI/ML solutions. Collaborate with product, UX, and engineering to deliver features, create tests and automation, instrument monitoring and dashboards, and participate in agile processes while continuously learning and improving team practices.

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.

This role involves designing, developing, and maintaining AI/ML products, collaborating with stakeholders, and ensuring model deployment on AWS. Responsibilities include creating APIs, establishing MLOps practices, and driving AI strategy for operational improvements.

Lead design, development, and production deployment of ML models and GenAI/LLM solutions to optimize clinical trial workflows. Build scalable pipelines, monitoring, and CI/CD for robust production use. Collaborate cross-functionally, mentor junior engineers, and communicate technical results to technical and non-technical stakeholders.

Lead and mentor engineering teams while owning platform architecture (React, Node/Express, Postgres, AWS). Drive TypeScript migration, design ETL and AI/LLM integration frameworks, ensure reliability and observability, manage delivery, incidents, and hiring, and define technical strategy for production ML/AI systems.

Build and productionize LLM-powered tools, RAG systems, and agentic workflows. Deploy and operate AI infrastructure on Kubernetes, manage model serving and vector stores, integrate identity and access controls, automate CI/CD and data pipelines, and establish evaluation and observability for deployed services in a defense-oriented environment.

Lead development and refinement of machine learning pricing models (especially reinforcement learning) for Airbnb hosts. Collaborate with data scientists, product, and engineers to design, test, integrate, and monitor models, analyze performance, and prioritize projects to improve host outcomes.

Design, develop, and test AI/ML and optimization software for energy research on HPC systems. Build and evaluate models (deep learning, probabilistic methods, large-scale inference), productionize solutions, collaborate with researchers, mentor juniors, author technical publications, and support multiple projects across the research-to-implementation stack.

Lead a team building large-scale ML model evaluation and orchestration frameworks for autonomous vehicles. Own data analysis workflows, dataset creation, statistical model evaluation, regression reporting, and tool development (AWS Kubernetes orchestration, ReSimulation). Provide technical leadership, mentorship, and manage project timelines and deliverables.

Design, build, and deploy production ML models (time series forecasting and NLP). Perform EDA, feature engineering, model evaluation, and translate business problems into ML solutions. Maintain production model-serving pipelines and collaborate across teams to deliver scalable data-driven systems.

Contractor role to design, harden and productionize ML and LLM systems for enterprise clients: build data/training pipelines, MLOps (deployment, CI/CD, monitoring, versioning), RAG and retrieval systems, benchmarking and cost control, short audits, and client-facing advisory work. Work as part of Janea teams on time-bound engagements.

Lead applied AI research to design and prototype Transformer-based Payment Foundation Models and generative AI solutions for fintech use cases. Drive self-supervised pre-training, fine-tuning, model optimization, benchmarking, and evaluation; collaborate cross-functionally to transition prototypes to production and ensure responsible, secure AI deployment while mentoring engineering teams.

Lead development and deployment of cutting-edge ML models for pricing, design and analyze experiments, collaborate with product and engineering, present findings to stakeholders, and lead technical projects to influence team output and business metrics.

Lead the design and delivery of a production-grade localization ML stack for autonomous vehicles: architect models for landmark-based localization across sensors, own data and evaluation pipelines, drive feature development to deployment, define failure modes and safety coverage, and mentor engineers while integrating cutting-edge multimodal and foundation-model techniques into robust, real-time localization systems.

Design, build, and operate production agentic AI systems on AWS Bedrock AgentCore using Strands/LangGraph. Implement tool integrations (MCP/Gateway), evaluation pipelines and LLM-as-judge CI gates, observability, cost/latency controls, and guardrails for agents operating on live dealer data.

Build end-to-end ML software for autonomy foundation models: data pipelines, training/evaluation workflows, model implementation (LLM/VLM/VLA), simulator integration, and low-latency serving and inference tooling for vehicle deployment.

Design, automate, and manage end-to-end ML pipelines and scalable infrastructure on Azure. Build CI/CD with GitHub Actions, containerize models with Docker and AKS, monitor model lifecycle and data drift, implement cost controls, and collaborate with data science, DevOps, and IT to deploy resilient, production-grade ML solutions.

Design, implement, and scale ML-based solutions for search and advertising; define data collection, evaluation, and production serving strategies; analyze data to inform development; communicate cross-functionally; produce technical reports; and stay current with advances in LLMs, vision models, and generative AI.

Design, implement, and own a reusable on-device ML inference platform for resource-constrained devices. Integrate inference into RTOS firmware, develop drivers and DMA paths, debug on real hardware, train and optimize models for microcontroller-class hardware, and ship features to production while raising team expertise and using AI-assisted tooling.

As a Senior Machine Learning Engineer, you will own the ML stack, develop generation systems, improve quality, collaborate cross-functionally, and build data pipelines.

As a Senior Machine Learning Engineer, you will enhance diffusion models, conduct research, collaborate on system implementations, and communicate findings effectively.

Partner with customers to design and deliver production-ready AI/ML solutions, drive POC-to-production success, troubleshoot technical issues, inform product roadmap with customer feedback, and communicate outcomes to stakeholders.

The Senior Data Engineer will own the data strategy, managing sourcing, structuring, and optimizing multimodal data to enhance AI models and products.

As a Senior Software Engineer, you'll build and operate the ML platform, optimizing data operations, managing the feature store, and supporting ML initiatives while driving architectural roadmap decisions.