Top Machine Learning Engineer Jobs

Develop and apply AI/ML techniques for anomaly detection in cybersecurity, build and optimize models and tools using Python, Pandas, and C/C++, analyze and troubleshoot security events, work with embedded DoD devices, and support on-site activities with quarterly travel while maintaining an active Secret clearance.

As a Machine Learning Engineer, you'll build ML infrastructure, develop scalable data pipelines for farm images, and collaborate with farmers and agronomists to apply ML solutions.

Lead bring-up, administration, and operations of a large GPU/AI training cluster. Serve as bridge between researchers and hardware, ensuring SLURM jobs, parallel filesystems, networking, and monitoring operate reliably. Work across provisioning, storage, VPN/access, and traditional Linux sysadmin tasks; assist with physical racking and on-site datacenter needs. Collaborate closely with a small research team in Tokyo or San Francisco.

Develop, debug, and maintain AI applications and tools for large-scale data analysis. Collaborate with engineers and leadership to design solutions, create documentation, and improve developer workflows using modern LLM tools.

Design, implement, and scale ML-based solutions for search and advertising; define data collection, evaluation, and production serving strategies; analyze data to inform development; communicate cross-functionally; produce technical reports; and stay current with advances in LLMs, vision models, and generative AI.

Design and implement scalable, secure cloud-native platforms and services for real-time network data processing, generative AI and agentic systems. Lead architecture, mentor engineers, build production-grade distributed systems for analytics, telemetry, and intelligent automation.

Build and operate production Kubernetes-based compute platform for large-scale data and ML workloads. Improve cluster reliability, GitOps delivery, multi-tenant scheduling, resource isolation, GPU workload orchestration, and reusable batch/workflow systems for petabyte-scale processing while contributing to QMS and continuous improvement.

As an ML Research Engineer, you'll optimize speech recognition and NLP models, integrate AI components, and develop knowledge graphs. Collaborate with experts, manage rapid experimentation, and publish research.

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.

Join Artian AI as an AI/ML Engineer to develop their innovative AI platform, focusing on enterprise process automation through agentic AI. Responsibilities include working with AI Workflow Agents, multi-agent systems, and various LLM technologies.

As a Senior Machine Learning Engineer, you will enhance diffusion models, conduct research, collaborate on system implementations, and communicate findings effectively.

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.

As a Senior MLOps Engineer, you will optimize machine learning models for performance and scalability, collaborate with R&D engineers, and implement CI/CD pipelines.

Design and build core systems for data processing and AI pipelines, ensuring reliability and scalability for enterprise clients while mentoring junior engineers.

The role involves bridging machine learning and systems engineering to enhance autonomy performance by optimizing data quality and developing frameworks for synthetic data in autonomous systems.

This role involves optimizing model serving layers for voice AI applications, working with inference engines, and improving performance for STT and TTS systems.

The Research Engineer will enhance RL training infrastructure, streamline research processes, and collaborate with teams to improve model deployment efficiency.

Design, build, and improve ML systems and infrastructure, mentor junior engineers, and write maintainable code in a customer-focused environment.

Design, build, and optimize high-speed interconnects for AI ML clusters, manage vendor relations, and drive innovation in connectivity solutions.

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

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

Architect and manage ML pipelines for training and deployment on cloud platforms, automate CI/CD, and ensure system performance and availability.

Responsible for architecting high-performance machine learning silicon, collaborating with software and design engineers, and driving innovation in AI hardware solutions.

The role involves designing and deploying AI/ML models, collaborating with stakeholders, and enhancing operational efficiency through innovative solutions.

Design, build, and maintain scalable, secure ML pipelines and infrastructure for training, evaluation, deployment, and monitoring. Implement CI/CD for models, manage data pipelines, feature stores, and model versioning, and integrate security practices. Support deployment in on‑prem, cloud, hybrid, and classified environments and collaborate with researchers to productionize ML systems.