Top Remote Machine Learning Engineer Jobs

Design, build, deploy, and operate production machine learning models across the full ML lifecycle: data exploration, feature engineering, model building, deployment, monitoring, and continuous improvement. Collaborate via pair programming and TDD; mentor junior engineers and lead ML initiatives at senior levels.

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.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

Lead end-to-end ML projects for automotive manufacturing: design, train, validate, deploy, and monitor models; implement MLOps/CI-CD; perform data engineering, EDA, and model evaluation; ensure data security and regulatory compliance; document and communicate results to stakeholders.

Design, build, and maintain scalable ML models and data pipelines to automate logistics processes. Own end-to-end projects from problem definition to production deployment and monitoring. Collaborate with engineering, mentor ML teammates, review code, and evaluate tooling to improve infrastructure and workflows that deliver customer value.

Develop, fine-tune, and deploy LLMs for NLP tasks; design and maintain large-scale data pipelines; analyze time-series data; implement data-driven models and OCR systems; ensure data quality; collaborate cross-functionally; and research and apply best practices to improve internal data processing tools and infrastructure.

Lead architecture and technical strategy for Lyra's ML and generative AI platform. Design training, fine-tuning, and low-latency inference infrastructure (RAG, vector DBs), set ML/AI SDLC standards (dataset lineage, CI/CD, evaluation, monitoring), partner with cross-functional clinical and product teams, mentor senior engineers, deliver prototypes and critical code, and ensure secure, reliable production ML systems.

As a Machine Learning Engineer/Scientist, you will develop ML systems for cryopreservation research, manage data pipelines, and deploy models for scientific insight.

Design and build AI assistants for interactive 3D workflows: build NLP pipelines, integrate cloud and client LLMs, design knowledge graphs, fine-tune foundation models, prototype AI features, and improve personalization and system performance.

The Lead AI/ML Engineer evaluates emerging technologies, leads teams in adopting solutions, and provides technical leadership while mentoring junior specialists.

As a Machine Learning Engineer, you'll design and implement detection systems, enhance model efficacy, and analyze datasets to improve email detection against security threats.

Design and deploy object detection and tracking modules; develop ML algorithms; guide team members; improve data governance and quality assessment.

Design and prototype state-of-the-art visual encoders and multimodal representations; train billion-scale models on GPU clusters; build visual reasoning and retrieval tools; collaborate on text-to-image and VLM projects; mentor junior researchers; publish research and ship models to production.

As a Senior Staff Machine Learning Engineer, you will drive AI product development, collaborate with cross-functional teams, and enhance ML models at scale.

Lead development and implementation of AI-driven solutions focused on text classification and NLP. Build and optimize ML models, preprocess and transform text data, combine supervised/active/semi-supervised approaches, create tagged datasets, and provide technical leadership while collaborating cross-functionally to align AI capabilities with product and business goals.

Embed with one or two Autonomy teams to enable large-scale replay/recompute workflows: implement data flows, onboard models, ensure metric evaluation and visualization adoption, debug full-stack issues, document workflows, and translate team feedback into simulation platform improvements.

Design, build, and deploy production-grade AI/ML models, agentic workflows, and data pipelines using Python, Databricks, LangChain/LangGraph, and Azure. Collaborate with cross-functional teams to translate requirements into scalable LLM applications, implement feature engineering and validation frameworks, and embed responsible AI practices including fairness, security, and monitoring.

Build and maintain data and model layers for an AI decision‑support system: data ingestion, probabilistic record matching, calibrated scoring, provenance‑tracked retrieval and grounded generation, and secure production pipelines. Ensure explainability, auditability, and accredited deployment in a controlled government cloud environment.

As a Staff Machine Learning Engineer, you will design and implement AI-driven solutions, build ML infrastructure, and lead teams to deliver scalable systems for real-time recommendations.

Design and build data and model layers for an accredited AI decision-support system: record matching, calibrated scoring, provenance-tracked retrieval and generation, secure ingestion pipelines, model packaging/serving, and instrumentation for audit and accreditation.

The Senior Machine Learning Engineer will develop and optimize AI image generation models, monitor production issues, and collaborate on ML solutions for generative features.

As a Machine Learning Engineer, you'll design and implement distributed systems for training large-scale ML models while optimizing performance under challenging conditions.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

The role involves designing and scaling ML infrastructure for LLMs, including building training pipelines and deploying models in cloud and on-prem environments. Collaboration with engineers and managing GPU/TPU workloads is key.

Lead the Scene Generation team to define technical vision and roadmap for neural rendering and generative models. Turn research (NeRF, 3D Gaussian splatting, diffusion/flow models) into production-quality camera/LiDAR/radar sensor simulation, own framework end-to-end, integrate and scale pipelines in cloud/VDI environments, drive cross-team alignment, and mentor/hire engineers.