Top Machine Learning Engineer Jobs

The Sr. Machine Learning Engineer will use ML and DL models to solve complex problems, mentor junior engineers, and assess algorithm performance while collaborating with a team.

The role involves designing and implementing agentic workflows for ML, building AI-native systems, and improving model development through automation and self-improving loops.

The role involves designing and deploying a data lakehouse infrastructure, building ML pipelines, monitoring services, and collaborating with teams to optimize AI systems.

Develop and evaluate quantization and sparsity recipes for LLM inference, run large-scale post-training and quantization-aware experiments, build evaluation and numerics-debugging tools, collaborate with inference framework teams, and publish or upstream improvements to production inference libraries.

Own and build distributed training and inference platforms for LLMs and multimodal models, implement production training algorithms and inference optimizations, design multi-provider routing and failover, build evaluation/experimentation infrastructure, lead cross-functional initiatives, mentor engineers, and improve latency and cost efficiency.

As a Senior Data Scientist, you will build ML models for skill estimation and matchmaking, collaborate with teams to enhance player experiences, and drive decisions for game development. Responsibilities include dataset creation and enhancing data science practices.

Lead architecture and implementation of scalable ML data pipelines and tooling to turn autonomy data into training datasets. Build data selection, active learning, inference-at-scale, vector DB management, labeling integration, QC automation, and introspection tools while partnering with ML researchers and infra teams.

Lead the design and development of AI-driven search and retrieval systems, focusing on machine learning applications and collaboration with cross-functional teams.

The Senior Machine Learning Engineer will design, prototype, build AI systems, evaluate ML models, and improve Vectara's AI Agents with a focus on reliability and performance.

This role involves developing and maintaining ML platforms for model experimentation and deployment, improving systems, and ensuring quality and observability for ML-driven products.

The role involves maintaining and enhancing ML applications on AWS EKS, developing APIs, implementing CI/CD pipelines, and collaborating across teams.

The Senior Machine Learning Engineer will design and implement ML systems, optimize data pipelines, and collaborate on ML workflows for personal injury legal solutions.

Intern will develop AI/ML applications for containerized production environments: analyze data, select and train models, build inference pipelines, prototype software architecture, debug customer issues, and collaborate with global ACS teams and product engineers.

The Senior Data Scientist will develop and deploy ML/AI systems for claims processing, focusing on model performance, integration of data sources, and collaboration across teams.

Build and enhance an ML platform supporting the full model lifecycle, scalable training and real-time prediction services, expand NLP capabilities, design data engineering solutions, maintain production reliability, and mentor junior engineers.

The role involves conducting R&D in machine learning, particularly in model training and architecture development. Candidates should have a background in deep learning and experience in hands-on experiments with foundational models.

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.

The role involves creating models for machine learning accelerators, analyzing workloads, and collaborating with engineering teams to optimize performance and functionality.

As a Senior Software Engineer, you'll build AI features focused on LLMs and RAG systems, collaborating across teams to ensure robust performance.

The Research Engineer will improve dataset quality for self-driving systems by designing algorithms for auto-labeling and data monitoring, collaborating with teams, and enhancing model performance.

Responsible for building and maintaining software that interfaces with networks, requiring expertise in network protocols, OS programming, and debugging distributed systems.

This role leads the ML Acceleration team, focusing on enhancing ML model training efficiency and team management, while driving technical strategy and collaboration.

Lead and manage a new ML Acceleration team, driving strategy and development for faster ML model training and optimizing performance.

Design and build ML infrastructure components for autonomous vehicles, ensuring scalability and robustness for high-volume data processing and model training.

Provide L2 24/7 production support for MLOps and data pipelines: troubleshoot Dataiku workflows, monitor ML workloads on AWS, manage CI/CD and containerized deployments, perform RCA and escalate to L3, and maintain monitoring, security, and compliance for production ML models.