Top Remote Machine Learning Engineer Jobs

Build and maintain scalable ML and generative AI platforms and backend infrastructure; deploy and manage production applications; create services exposing ML/AI products; implement RAG solutions and vector databases; integrate LLM orchestration and foundation model APIs; collaborate cross-functionally and mentor teammates.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

Develop and fine-tune vision-centric large language models and visual encoders, build evaluation benchmarks, collect and prepare visual training data (including for RLHF), prototype architectures for production VLMs, publish research, and mentor junior researchers within a small applied ML research team.

Develop and deploy end-to-end machine learning solutions, collaborate with cross-functional teams, and implement MLOps frameworks while monitoring model performance in production.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

Develop scalable web applications using React and Next.js, implement backend solutions in Python, maintain DevOps practices, and collaborate with AI/ML teams to deploy applications.

Design and operate ML training and serving infrastructure, build a Kubernetes+Ray backend, improve developer experience and observability, mentor engineers, and ensure reliable, secure deployments for low-latency, high-performance systems.

Build and harden low-latency, high-throughput ML platform infrastructure. Design storage, indexing, streaming, backpressure, failure handling, and observability. Scale decisioning, improve developer experience for data scientists, manage Kubernetes deployments, and collaborate with SRE, data infra, and engineering to ensure reliable, debuggable production systems.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving AI solutions.

Lead design, development, and deployment of advanced machine learning models and infrastructure. Own architecture, data pipelines, experimentation, validation, and team technical leadership to drive AI-driven product features and business value.

Develop and prototype large-scale text-to-image generative models (Pinterest Canvas), collaborate on multimodal embeddings, collect and prepare visual training data (including RLHF and fine-tuning), ship models to production, publish research, and mentor junior researchers.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

Lead end-to-end ML projects for fraud detection: develop, deploy, monitor, and iterate production models and experimentation frameworks. Improve model performance, build scalable ML pipelines, collaborate with Product/Engineering/Risk, and champion testing, documentation, and model-monitoring best practices to ensure robust, high-impact solutions.

Design, build, and deploy high-throughput ML and LLM-powered personalization and agentic systems at consumer scale. Process petabyte-scale data, optimize real-time inference, implement resilient fallback paths, and balance latency, cost, and model quality while collaborating across engineering and product teams.

The Principal AI/ML Engineer will lead the design, implementation, and deployment of AI systems, mentor junior engineers, and ensure model performance and reliability in production environments.

As a Machine Learning Engineer in Computer Vision, you'll develop AI solutions using advanced algorithms, focusing on image classification, segmentation, and video processing tools.

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

Develop and deploy ML-based perception algorithms for robotics, optimize pipelines, and collaborate across disciplines for integration.

The Staff ML Engineer will lead the architecture and development of AI/ML features, oversee complex systems, mentor teams, and enhance product experiences through ML solutions.

Design, build, and operate scalable MLOps infrastructure for data ingestion, distributed training, deployment, CI/CD, experiment tracking, and monitoring across cloud R&D and production. Collaborate with ML, autonomy, robotics, and software teams to deliver architecture, PoCs, and production-ready ML pipelines that support real-time, safety-critical systems.

Design, build, and productionize personalized recommendation models and LLM-based experiences for Spotify's Home feed. Own model training, fine-tuning, evaluation, A/B testing, inference optimization, and ML platform/data pipeline improvements. Drive technical direction, mentor engineers, and collaborate with cross-functional partners to deliver scalable, cost-efficient personalization at global scale.

The Staff Engineer will design and manage ML infrastructure while mentoring teams, driving MLOps strategies, and ensuring compliance in healthcare data systems.

Join Sajix as a Machine Learning Engineer Trainee to assist in building and deploying ML models for healthcare solutions, collaborating with data scientists, and gaining hands-on experience.

Design and deploy machine learning systems for autonomous vehicles, collaborating with teams to improve model performance and safety using real-world data.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.