Top Machine Learning Jobs

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.

The Machine Learning Researcher will develop new architectures for data-efficient model induction, focusing on optimizing generalization performance and runtime inference in networks, while contributing to a performance-driven code base.

The Senior Applied Scientist will develop machine learning solutions for personalization, pricing optimization, and fraud detection, leading model development and experimentation while collaborating with various teams.

The Machine Learning Engineer will design and build scalable ML data pipelines, manipulate 3D data, collaborate with customers, and ensure reliable deployment of ML systems.

The Senior Machine Learning Engineer will design, deploy, and mentor on ML models, collaborate with customers, and influence product direction. Responsible for robust ML systems and data pipelines.

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

The Applied ML Engineer will research and build AI/ML systems, own model development, partner with teams to impact marketplace metrics, and establish best practices for AI/ML at Paraform.

As a ML Engineer at Apiphany, you will develop advanced machine learning models, focusing on engineering data, AI systems, and real-world performance.

Design and implement 3D machine learning models, process reality capture data, and integrate AI solutions in production environments for robotics applications.

The Machine Learning Engineer role at Wayve focuses on leading initiatives in model-based autonomous driving, involving the development and delivery of ML-driven behaviors, collaboration across teams, and mentoring other engineers.

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 Staff Machine Learning Engineer will design and implement ML systems, build scalable architectures, collaborate with data teams, and drive technical strategy for AI applications.

The Staff Machine Learning Engineer will optimize the inference layer for voice applications, focusing on model serving and real-time audio processing. Responsibilities include driving performance architecture, collaborating with model partners, and developing evaluation frameworks for STT and TTS models.

Lead end-to-end machine learning solutions for League of Legends, from problem framing to production. Build personalization, recommender, matchmaking, and in-game ML systems; design evaluations and experiments; integrate models with game and service engineering; create reusable ML patterns; mentor engineers and improve ML observability, monitoring, and launch practices.

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.

The Machine Learning Engineer will build and scale ML infrastructure, drive model performance, and ensure reliability while collaborating with various teams to optimize feature engineering pipelines and champion best practices.

The Senior Engineer II, Machine Learning will develop and implement machine learning models, analyze multiomic/single-cell data, and apply image processing techniques for research projects.

Architect and own the ML roadmap for continental-scale grid intelligence. Design and deploy multimodal ML, GNNs, physics-informed models, and RL for grid planning, simulation, and operations. Mentor senior engineers, set production standards, and collaborate with power-systems scientists and utility partners to deliver scalable, reliable ML systems.

Build and improve production ML and LLM-powered systems to raise assistant and agent quality. Design evaluation, benchmarking, and monitoring; develop signals, prompts, workflows, and model-driven logic; work on RAG, retrieval, personalization, RL, and orchestration; partner with product and engineering to ship production systems and support data/ML infrastructure for experimentation and continuous improvement.

Lead technical strategy and architecture for large-scale ML systems powering embeddings, retrieval, recommendation, ranking, and notifications. Build and ship production models (GNNs, bi-encoders, cross-encoders), set ML standards, mentor engineers, and collaborate cross-functionally to drive responsible, explainable AI and measurable product impact.

Design, implement, and evaluate ML and signal/image processing algorithms for geospatial/intelligence sensor data. Develop data ingestion, visualization, and scalable software tools, collaborate with teammates and customers, document results, and support deployment on customer systems. Apply statistical techniques to assess algorithm performance.

Design, build, and deploy end-to-end machine learning solutions for growth marketing platforms. Develop scalable model training, validation, and serving pipelines, lead experiments, present results to leadership, and mentor junior engineers to improve customer acquisition, retention, and ROAS.

Pioneering machine learning solutions for the defense sector, developing and deploying production-grade ML models, tackling complex data challenges, and collaborating with engineers.