Top Machine Learning Engineer Jobs

The role involves building machine learning systems for Radar's products, focusing on core ML infrastructure, anomaly detection, geolocation, and collaboration with customers.

The role involves developing ML systems, designing low-latency infrastructure, and collaborating on AI/ML initiatives while ensuring reliability and performance at scale.

As a Senior Machine Learning Engineer, you will design and deploy computer vision models, collaborate with teams, and mentor juniors while ensuring high-performance solutions.

The role involves building and optimizing a machine learning and simulation platform, supporting MLOps, model deployment, and infrastructure modernization, while mentoring engineers.

As a Senior ML Infrastructure Engineer, you'll design and build scalable platforms for ML inference workflows, collaborating with teams to optimize model serving and enhance system reliability.

Lead research on machine learning validation for robotics and autonomy systems, focusing on simulation, uncertainty modeling, and creating functional prototypes for testing.

As a Staff ML Infra Engineer, you will develop and deploy offboard machine learning solutions for autonomous vehicles, ensuring model integration and performance across teams. You'll build ML infrastructure, implement CI/CD pipelines, support data curation, and mentor engineers.

The role involves training and deploying ASR models, improving pronunciation feedback, measuring performance, and expanding ASR systems for language learning.

As an ML Engineer, develop and deploy advanced ML solutions for OCR, document processing, and voice technologies, collaborating with teams to optimize models for performance.

The Principal ML/AI Engineer will shape the AI vision, prototype AI systems, and architect an AI-ready technical stack while mentoring teams and collaborating with executives.

As a Senior Applied ML Engineer at Macroscope, you will design, build, and optimize ML/AI systems, leading the AI Code Review process and ensuring integration with backend systems.

Develop and improve machine learning models and systems for clinical outcomes. Lead ML initiatives, mentor teams, and establish best practices in AI. Collaborate across engineering, product, and design for data-driven solutions.

Develop and implement machine learning algorithms for defect detection in high-resolution images. Optimize real-time performance and evaluate model efficiency.

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

Design, build, and deploy AI systems, manage the ML lifecycle, collaborate with teams, and advocate for new techniques in machine learning.

Develop, optimize, and deploy computer vision models (object detection, tracking, segmentation, facial recognition). Prepare and preprocess large image/video datasets, evaluate and monitor model performance in production, and collaborate with cross-functional teams to integrate scalable vision AI solutions.

Develop and implement AI and ML systems for Defense and Intelligence sectors, focusing on deployment, scalability, and integration of technologies.

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

As a Principal Machine Learning Engineer, you will architect AI solutions, collaborate with scientists, and optimize models for edge deployment. Your work enhances public safety applications.

Build, deploy, and monitor ML/AI models end-to-end: research and implement algorithms, design ML approaches, analyze and visualize data, ensure model performance in production, collaborate with stakeholders and cross-functional teams, and mentor junior engineers.

Build, deploy, and monitor ML/AI models to automate processes and generate insights. Collaborate with stakeholders, Data Engineers, and Product Managers to design approaches, train and compare models, analyze data distributions, detect adversarial trends, and deliver production-ready solutions aligned to business KPIs.

Design, develop, and deploy enterprise ML products and pipelines; apply ML/NLP algorithms and statistical methods; monitor and improve model performance; visualize and report findings; collaborate with data engineers, scientists, and product managers; mentor junior team members.

Design, develop, and maintain core ML inference platform components including model deployment, optimization pipelines, and benchmarking/simulation workflows. Collaborate with systems and cloud engineers, and build APIs/tools to ensure scalable, reliable, and hardware-efficient inference solutions.

Design, develop, and deploy production ML models and scalable pipelines. Collaborate with cross-functional teams, optimize model performance, implement MLOps deployments, monitor model health, and document experiments and metrics.

Lead design and implementation of LLM observability features: prototype and scale product capabilities for tracing, evaluating, and debugging generative AI systems. Work cross-functionally to influence architecture, mentor engineers, prioritize customer pain points, and drive product and engineering decisions for reliable, high-performance AI observability.