Top Machine Learning Engineer Jobs

Design, build, and maintain scalable AI/ML architectures; collaborate with data scientists, engineers, product, and compliance to integrate models into products; evaluate tools and ensure performance, quality, and compliance while staying current on AI/ML advances.

Own end-to-end data science and ML work for B2C customers: build recommendation, forecasting, segmentation, and activation models; design A/B tests and causal analyses; create configurable multi-tenant model architectures; extract signal from sparse/noisy data; partner with engineering to productionize models and deliver analyses that change business decisions.

Build and improve AI/ML systems and data pipelines for a B2B SaaS platform; shape product and technical strategy; work with customers; mentor team members; ensure secure, robust, and scalable infrastructure.

Design, develop, and deploy machine learning models; analyze large datasets; build scalable data pipelines and distributed computing environments; integrate ML solutions with cross-functional teams; optimize performance and support production ML infrastructure.

Design, develop, test, and deploy machine learning models and pipelines for manipulation, navigation, locomotion, and perception in humanoid robots. Monitor model performance in real-world settings, collaborate with partners and universities, and help grow the ML and autonomy teams.

Own Coinflow's ML layer: explore transaction and behavioral data, build production models and decisioning systems, establish ML/data practices, define core metrics, and partner with Engineering, Product, and Operations to embed intelligence into products and tooling.

Fine-tune and optimize LLMs (including multi-modal) for humanoid robots and autonomous vehicles; build scalable training and deployment pipelines; work on model efficiency (quantization, weight sharing), synthetic data generation, and cross-team product alignment.

The role involves applying machine learning techniques to circuit design, analyzing datasets, building and optimizing models, and collaborating with teams on various projects in VLSI and circuit design.

Research Engineer will develop and optimize machine learning algorithms for defense applications, focusing on edge devices and enhancing autonomy for military systems.

As a Research Engineer in Machine Learning at Anduril, you will optimize ML algorithms for edge devices, prototype LLM-based systems, and benchmark models while collaborating across business lines to identify new research problems.

The Research Engineer will develop and benchmark ML algorithms, optimize transformer architectures for edge devices, and collaborate on research projects across teams.

Responsible for designing, building, and maintaining machine learning models in production, collaborating with cross-functional teams and ensuring best practices in ML engineering.

Design and build production systems for AI inference, optimize runtime services, and collaborate on AI solutions with researchers and engineers.

Seeking a Machine Learning Engineer with 3 years of experience to develop and deploy ML systems, optimize models, and work collaboratively with teams to enhance ML capabilities.

Develop advanced ML solutions to improve Stripe's payment products. Work on the ML lifecycle from research to deployment, collaborating with product teams.

As an ML Engineer, you'll deploy models and create tailored AI solutions for clients, ensuring impactful integrations and customer success.

Design, test, and build infrastructure for ML Ops lifecycle in the insurance industry. Develop and deploy AI and ML solutions affecting billions in transactions.

Design and maintain scalable machine learning infrastructure, collaborating with data scientists, ensuring performance, security, and compliance of AI systems.

Summer PhD internship building and deploying production ML models for search, recommendations, and matching. Collaborate with engineers and data scientists on experimentation, model evaluation, monitoring, and documentation to improve product discovery and user experience.

Design, implement, and deploy ML models and scalable data pipelines for multimodal behavioral and clinical data. Clean, integrate, analyze, and visualize time-series and wearable data; build real-time dashboards/apps; evaluate model performance; supervise analyses; collaborate with IT; and support publications and grant preparation.

Lead design, implementation, and productionization of the platform's ML memory layer and LLM strategies. Build and operate training, fine-tuning, inference, monitoring, and deployment pipelines; collaborate cross-functionally to ensure scalable, reliable ML systems that improve with data and integrate deeply into product workflows.

Productionize research models into performant, maintainable systems; build CI/CD and automated MLOps pipelines; optimize inference and monitor for data/concept drift and latency; improve AVM and recommender systems; build data products and tooling; serve as technical bridge between research and engineering to drive production-scale, customer-facing ML features.

Lead design, build, and maintain scalable generative AI and LLM-based agent solutions. Collaborate cross-functionally, integrate with GEICO's AI platform, drive technology selection, ensure production reliability, and mentor engineers to deliver business-facing AI applications.

Lead design, development, and deployment of scalable generative AI solutions and agentic workflows. Collaborate with cross-functional teams, integrate with GEICO's AI platform, evaluate and select technologies, own project planning and stakeholder management, and mentor engineers to deliver production-grade LLM-based agents, RAG pipelines, and robust monitoring and maintenance.

Design, build, and deploy scalable ML systems (NLP, Computer Vision, Generative AI); architect ML infrastructure and pipelines; implement MLOps, CI/CD, monitoring, model optimization, and distributed training; collaborate cross-functionally to productionize models and improve dataset quality.