Top Remote Machine Learning Jobs

Conduct research and develop identity and probabilistic-matching models for Netflix's ad-supported tier. Apply ML and statistical methods, ensure privacy-preserving solutions (differential privacy, anonymization), prototype and deploy production models, deliver datasets and tools, and partner with product, engineering, and business stakeholders to measure and drive impact.

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.

The Machine Learning Scientist will research, develop, and deploy machine learning solutions for real-time healthcare decision systems focusing on maternal health. Responsibilities include evaluating methodologies, collaborating with teams, and achieving product development goals.

The Junior-Midlevel Developer will build and enhance Dumroo.ai's AI-powered education platform, collaborating on software components, data pipelines, and machine learning features.

Build, evaluate, and deploy LLM/ML models and synthetic datasets to improve Community Support. Identify opportunities via data exploration, analyze structured and unstructured data, automate LLM evaluation, personalize experiences, and collaborate cross-functionally to drive product and operational impact.

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.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

Create and evaluate mathematics-related content, perform research and algorithm development for ML/LLM applications, solve mathematical problems, and mentor junior team members while collaborating with cross-functional teams.

Own and scale delivery of complex AI data programs (LLM/ML products), ensuring scope, quality, timeline, and margin targets. Translate ambiguous problems into technical workstreams, establish governance, drive solution reviews, embed responsible AI practices, manage vendors, and lead cross-functional teams while partnering with BD and executive stakeholders.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.

Design, build, and deploy novel sensor hardware and data acquisition systems; curate geoscientific datasets; develop statistical, physics-based, and ML models (2D/3D) to predict mineralization; create visualizations and evaluation tools; collaborate with geoscientists, engineers, and vendors; deploy sensors in the field and integrate results to guide exploration programs.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

The Senior Machine Learning Engineer leads technical direction for ML systems focused on personalization and ranking, overseeing architecture and implementation while mentoring team members.

Lead development of interpretable, production-quality valuation models for residential real estate. Partner with the CTO to design methodologies, quantify uncertainty, evaluate model performance, and translate research into scalable, maintainable ML systems. Mentor hires, set technical standards, and collaborate with engineering to deploy trusted analytics used across the country.

Act as the technical lead in enterprise sales: run technical discovery, design and deliver customized demos and POCs, assess API/cloud/model fit, address technical objections, support contract negotiations, and relay product feedback to engineering while staying current on AI/ML trends.

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.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Design, build, and own a multi-tenant enterprise SaaS backend that feeds ML data and BI. Collaborate with product, data science, UX, and users to deliver scalable, reliable customer-facing AI solutions. Drive systems design, database schemas, development lifecycle standards, technical docs, and code reviews while using AI/agentic tools to accelerate development.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

Lead design and production deployment of ML models focused on pricing, availability, forecasting, and optimization. Build data infrastructure, governance, testing/monitoring, and dashboards. Partner with executives and cross-functional teams, mentor others, and establish scalable data-science practices and experimentation frameworks.

Lead and grow a team of ML researchers and engineers to define strategy and roadmap for next-generation recommendation systems. Translate cutting-edge research into production systems, collaborate cross-functionally, mentor talent, and drive metrics-backed product outcomes using state-of-the-art ML and generative techniques.

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 development and implementation of machine learning solutions, managing a team to drive strategic decisions and innovative business outcomes.