Top Machine Learning Engineer Jobs

Design, implement, and scale production-grade data and ML pipelines for risk and payments. Build ETL and feature engineering workflows, productionize models, establish observability and monitoring, participate in reviews and on-call rotations, and mentor junior engineers to ensure reliable, explainable decisioning systems.

Design, build, and deploy production-grade AI/ML capabilities (NLP/LLMs, generative AI, computer vision) across the ML lifecycle. Work on EO/IR signal-processing, GPU-optimized pipelines, streaming/NRT data, MLOps, and microservice-based deployments in classified environments with mission teams.

As an AI/ML Engineer, you'll implement machine learning techniques, build AI models, and support national security applications, collaborating with various teams.

Design, build, and deploy production-grade AI/ML systems (NLP/LLMs, computer vision, generative AI). Create scalable data pipelines, microservices/APIs, and cloud- or edge-deployed models. Ensure security, observability, and performance; perform model optimization, versioning, A/B testing, and documentation. Collaborate with engineers, mission SMEs, and product teams to meet operational requirements.

Design, implement, and maintain large-scale real-time ML systems for an AI-powered DSP. Run experiments, build models (NLP, clustering, LLMs, recommendation), optimize performance, and collaborate with engineering teams to deliver scalable production ML products.

The ML Engineer will build and deploy machine learning models to optimize operations in manufacturing, including demand intelligence and production monitoring.

As a Senior AI/ML Engineer, you will design and deploy LLM systems, collaborate with cross-functional teams, and manage model deployment in production environments.

The Junior AI/ML Engineer will deliver machine learning projects, handle Python programming, and learn from a senior team member, focusing on real impact initiatives.

Develop and deploy manufacturing intelligence systems and machine learning models to enhance operational efficiency in production environments, collaborating across teams to achieve impactful solutions.

Lead the navigation ML workstream for autonomous driving systems, overseeing model training and deployment, defining technical direction, and mentoring junior engineers.

The role involves developing, training, and deploying machine learning models, building data pipelines, and collaborating with teams for integration and performance optimization. It supports the ML lifecycle from data collection to monitoring.

The role involves designing, developing, and deploying machine learning models for healthcare solutions, optimizing data processing pipelines, and collaborating with cross-functional teams to enhance patient care.

As a Fraud ML Engineer, you will ensure authentic human data is processed by building detection systems using adversarial ML techniques and managing backend systems.

You'll lead the development of AI-driven systems for a dating platform, focusing on personalized matching algorithms and voice interactions. Responsibilities include designing ML pipelines, deploying models, and conducting experiments to improve user experience.

As an AI/ML Architect, you will define scalable AI systems architecture, mentor teams, guide clients, and ensure ethical AI practices.

As an HR AI/ML Engineer, you will develop AI solutions for HR processes, conduct data analysis, maintain AI models, and ensure data integrity.

Lead the design and implementation of Machine Learning infrastructure for Voice AI products. Shape technical vision, mentor engineers, and collaborate on innovation.

Build and maintain MLOps pipelines, deploy and monitor ML models as scalable services, transform and integrate healthcare data (FHIR/HL7), ensure reproducible feature management, and uphold HIPAA/HITRUST security for production ML in clinical systems.

Architect, build, and operate ML inference and deployment infrastructure (model serving, orchestration, CI/CD, feature stores). Improve developer experience, optimize GPU/CPU scheduling and performance, and establish ML deployment best practices across Riot’s production ML ecosystem while shaping long-term platform direction.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Build and automate large-scale ML data pipelines and tooling for autonomous trucking. Improve dataset curation, deduplication, labeling, evaluation, and metrics infrastructure. Collaborate with robotics, autonomy, and infra teams to support continuous learning, deployment, and scalable AI infrastructure for Kodiak's fleet.

Lead design, development, and production deployment of scalable ML systems across domains. Own end-to-end ML lifecycle, architect pipelines and serving, drive MLOps maturity, mentor engineers, conduct design/code reviews, and ensure monitoring, governance, and model reliability in production.

Design, build, and deploy ML models and end-to-end pipelines; implement MLOps practices; deploy containers to cloud production; monitor model performance and drift; collaborate with engineers and product teams; optimize latency and throughput; write production-quality code and mentor junior engineers.

As a Senior AI Engineer, you will develop and optimize Generative AI applications, manage AI pipelines, collaborate with clients, and ensure production delivery with MLOps best practices.

The Senior Software Engineer will innovate in AI, lead software development lifecycle, design microservices, mentor peers, and ensure engineering excellence.