Top Machine Learning Engineer Jobs

Develop, optimize, and deploy machine learning models and pipelines for scalability. Build ML platform tools (Kubernetes, Helm, EKS), manage large datasets, design ETL/ELT processes, implement predictive modeling with gRPC, adapt research to production, and collaborate with cross-functional teams and customer success.

Design, build, deploy, and operate production machine learning models across the full ML lifecycle: data exploration, feature engineering, model building, deployment, monitoring, and continuous improvement. Collaborate via pair programming and TDD; mentor junior engineers and lead ML initiatives at senior levels.

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.

Design and implement scalable backend services and distributed, event-driven data pipelines. Build, deploy, and monitor production ML models (feature engineering through deployment). Integrate ML models with rule-based decision engines and contribute to AI/LLM-driven orchestration. Collaborate across engineering, data, and product teams to deliver reliable, production-grade systems.

Design, build, and deploy scalable AI/ML solutions and agents to monitor and optimize public cloud resources. Integrate ML insights with multi-cloud providers, create ETL pipelines, use Snowflake and Athena for data processing, automate workflows with Airflow, and ensure secure, high-performance cloud deployments while mentoring engineers and improving developer productivity.

Lead research, design, and deployment of large-scale foundation models for machine-generated data (logs, time series, traces). Define AI strategy, mentor senior engineers, partner with product and exec teams, and drive production-grade model training, optimization, and MLOps for enterprise-scale applications.

Design, prototype, and implement AI/ML solutions; evaluate and adopt AI tools and platforms; build end-to-end scalable cloud-based ML applications; analyze large datasets, visualize results, and provide technical consulting and stakeholder communication.

Build and deploy autonomous AI agents and GenAI solutions at scale. Design and operationalize distributed data flows, agent orchestration, memory/RAG systems, and integrations with LLM APIs. Collaborate globally, prototype state-of-the-art ML/GenAI technologies, and ensure scalable, reliable cloud-native architectures.

Lead advanced AI research (Generative AI, Agentic AI, cognitive architectures, LLMs, call transcription); develop new algorithms and prototypes; transition research to production with engineering teams; publish and present at top-tier conferences; mentor junior researchers; align research with business needs and Responsible AI principles.

Design, train, test, deploy, and maintain ML models for national-security cyber applications. Collaborate with software engineers and data scientists to build LLM-based automation, fine-tune open-source models, containerize solutions, and define ML strategy for mission-critical systems while meeting security requirements.

Design, build, and deploy AI systems across the full lifecycle: data pipelines, model development (traditional ML and generative AI), agent-based applications, API integration, observability, and evaluation. Collaborate with architects and domain experts, support experimentation and production deployments, and assist with project coordination and stakeholder communications.

As an ML research engineer, you will develop systems using language models, curate datasets, set evaluation metrics, and scale semantic search processes.

Lead the technical direction of a computer vision platform for sidewalk inspection. Responsibilities include managing ML models, improving data pipelines, and architecting systems for terabytes of geospatial data.

The role involves designing and optimizing ML infrastructure for large language models, collaborating with teams, and implementing best practices in ML and data engineering.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

Design and build end-to-end software products for autonomous vehicles, leading projects in full-stack development, cloud infrastructure, and machine learning pipelines while mentoring junior engineers.

As a Staff AI/ML Full-Stack Engineer at GM, you'll design and develop end-to-end software products, own technical projects, and mentor junior engineers while focusing on cloud infrastructure and scalable applications.

Develop and deploy machine learning solutions for autonomous driving, collaborating with cross-functional teams and mentoring engineers for technical excellence.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

As a Senior Machine Learning Engineer, you will lead model validation for AI systems, challenge model soundness, and build validation tools for high-stakes areas such as credit and fraud prevention.

The Staff Machine Learning Engineer will design, train, and deploy NLP models, optimize processes, and mentor engineers. The role demands expertise in model training and productionization.

You will build and maintain assessment ML systems, define evaluation frameworks, and collaborate with design teams to improve language proficiency assessments.

The AI Research Scientist will develop and evaluate generative models for scientific data, collaborate with teams to apply AI in materials science, and mentor junior members while staying current in the field.

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

Design and implement AI solutions for Marine Corps operational needs using various AI technologies. Mentor team members and apply best-fit ML algorithms to solve complex problems.