Top Remote Machine Learning Jobs

The Machine Learning Researcher will build predictive models using deep learning, explore strategies to enhance portfolio performance, and update models based on research. They should have a strong foundation in ML with hands-on experience in relevant domains.

Build core, shared ML capabilities for a production platform: foundation models for documents, extraction agents, model routing, unified evaluation, and continuous improvement pipelines. Own end-to-end capability development, production deployment, and cross-team support to improve accuracy, latency, cost, and reliability across institutional workflows.

Design, develop, implement, and monitor statistical and machine learning models to support credit risk, collections, servicing, marketing, and forecasting. Build data pipelines, translate business needs into analytical solutions, support model governance, testing, deployment, and ongoing performance monitoring, and communicate results to stakeholders.

Lead development and deployment of predictive and ML models for credit risk, collections, and operations. Mentor data scientists, promote MLOps and modern tools, present insights to stakeholders, and drive experimentation with techniques such as causal inference and GenAI.

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.

As a Senior Machine Learning Engineer, you will develop and deploy AI-driven products to enhance clinical trials, collaborating with teams to improve patient care through scalable machine learning solutions.

The Senior ML Engineer will architect data pipelines, develop classification algorithms, manage data integration, and mentor team members in a cross-functional setting.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

As an ML Engineer at Luma AI, you will design high-performance model serving pipelines, manage GPU resources, and optimize CI/CD for large-scale AI systems.

Lead the Machine Learning strategy at Gather AI, focusing on computer vision, building teams, and enhancing ML systems for warehouse intelligence.

The role involves designing scalable distributed systems using Scala, enhancing feature stores for performance, and collaborating with ML engineers to resolve challenges.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

Lead end-to-end development of fraud and risk ML systems: define problems, engineer features, build and evaluate models, collaborate with product and engineering, deploy with ML engineers, and monitor production performance to detect drift and trigger retraining.

Lead AI/ML consulting engagements: design and implement ETL, data modeling, predictive models, anomaly detection, distributed data pipelines, and executive-ready visualizations. Drive business development, mentor teams, and deliver client-facing solutions in hybrid/onsite environments.

Lead AI/ML consulting engagements, build and deploy machine learning models and ETL pipelines, analyze large datasets, design data architectures, deliver visualizations, drive business development, and manage client-facing teams in high-stakes situations.

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, evaluate, and deploy predictive ML models across regression, classification, NLP, computer vision, and time-series problems. Perform data cleaning, feature engineering, EDA, model selection, tuning, explanation (SHAP, feature importance), and collaborate with engineers and stakeholders to operationalize and monitor models in production.

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.

Design and build machine-learning-driven robotic systems that operate in the real world, support portfolio company growth, influence product and technical direction, and advance company and career outcomes.

Senior Machine Learning Engineers will develop ML products and lead projects end-to-end—from initial concept through final launch—across S32 portfolio companies. Candidates should drive product direction, have strong ML engineering and productization experience, and make significant business impact.

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.

As a Machine Learning Scientist at Nucs AI, you will develop innovative ML methods for medical image analysis, collaborate with clinicians, and contribute to clinical validation studies, impacting cancer diagnostics.

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.

Design and develop machine learning systems for construction robots, including scalable infrastructure, computer vision models, and data analysis for performance improvement.