Top Machine Learning Engineer Jobs

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 Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

Design, deploy, and maintain end-to-end ML risk solutions at scale to detect and prevent fraud, merchant risk, and credit loss. Partner with cross-functional teams, lead technical decisions, build ML tooling, monitor models in production, and investigate emerging abuse patterns to improve detection and decisioning.

The role demands a Senior Software Engineer skilled in backend development and ML, focusing on delivering ML solutions that address business challenges across various domains.

The role involves designing and developing a distributed ML data platform for autonomous vehicles, collaborating with stakeholders, and mentoring engineers while ensuring operational excellence.

Lead the design, development, and implementation of machine learning applications; collaborate with Agile teams to optimize performance and scalability.

Develop scalable ML platform infrastructure, support ML training and deployment, collaborate on software tools for scientists, and enhance workflows and automation.

Lead the development of healthcare data products using machine learning. Design models, collaborate with teams, and mentor others in statistical methods.

Lead development of advanced recommendation systems, driving ML innovation, maintaining ML pipelines, and collaborating across teams to enhance user engagement.

As a Machine Learning Engineer, you will build and evaluate AI agents for enterprise workflows, focusing on techniques for intent detection and agent quality metrics.

The ML Engineer will build scalable data architectures and ML pipelines, implement MLOps best practices, ensure data quality, and integrate ML systems with enterprise platforms. They'll work on both data engineering and ML model lifecycle management.

This role involves building and scaling machine learning systems for content understanding, improving quality, and collaborating with cross-functional teams to enhance user experience on Spotify.

Responsible for fine-tuning state-of-the-art LLMs, optimizing models for deployment, and driving AI architectural decisions. Collaborate with cross-functional teams to develop impactful AI products.

As a Principal Machine Learning Engineer, you'll design and implement core ML systems for AI agents, collaborating with cross-functional teams and overseeing the full lifecycle of products from data strategy to deployment.

As a Machine Learning Engineer, you will manage multimodal perception, work on data collection, and evaluate robotics tasks, directly impacting model capabilities and the evolution of physical labor.

The AI/ML Engineer will design and deploy ML models, optimize performance, collaborate with teams, and evaluate AI systems for decision-making support in defense.

Apply deep learning to improve quality and accuracy of labeled multi-modal sensor data for simulation. Build models to identify and correct perception-data issues, analyze data quality and anomalies, collaborate with perception and simulation teams, and lead ML strategies for high-fidelity data generation and labeling pipelines.

Design, develop, deploy, and maintain AI/ML models and data pipelines; collaborate with cross-functional teams; implement MLOps, monitor model performance, resolve incidents, validate models, ensure security/privacy/ethical compliance, and document solutions for federal customers.

Design, develop, deploy, and sustain agentic AI and ML capabilities across the full lifecycle. Build ML models and pipelines, integrate LLMs/agentic workflows, implement CI/CD and containerized deployments, evaluate system safety and performance, and collaborate with engineers and stakeholders to deliver secure, production-ready AI solutions for mission use.

Design, develop, deploy, and sustain agentic AI and ML capabilities. Build ML models and pipelines, integrate LLMs and agentic workflows, implement CI/CD and containerized deployments, ensure security and operational suitability, and collaborate across engineering and analytics teams to deliver mission-focused AI solutions.

Lead design, implementation, testing, and maintenance of large-scale AI/ML solutions. Develop and evaluate ML algorithms and predictive models, manage datasets and pipelines, integrate models into enterprise systems, document work, and mentor junior team members.

Design, build, and deploy production-grade AI/ML systems (including LLMs and agents) for defense and intelligence clients. Work spans model training, multi-modal deployments, MLOps, cloud (AWS/Azure), containerized deployments, observability, and architecture tradeoffs. Support integration with APIs, databases, and mission environments while meeting clearance requirements.

Design and implement scalable ML applications, collaborate with data scientists and engineers, perform data preprocessing, feature engineering and model evaluation, and deploy models using GCP/Vertex AI with BigQuery and NoSQL databases like MongoDB.

Lead technical direction for applied ML and AI Context systems across HubSpot. Build and productionize models, architecture, retrieval, feature pipelines, evaluation, experimentation, and monitoring. Mentor engineers, collaborate cross-functionally, and address privacy, bias, reliability, and data governance for customer-facing AI features.

Design, build, and maintain scalable AI/ML pipelines for data processing, model training, and deployment. Collaborate with data scientists and engineers to implement and optimize AI solutions, troubleshoot pipeline issues, document best practices, and develop MVP code to transition into production-ready systems.