Top Machine Learning Engineer Jobs

The Senior ML Infrastructure Engineer will own and optimize GPU infrastructure for training tabular foundation models, ensuring high efficiency and collaboration with research teams.

As a Senior Machine Learning Engineer, you will design and maintain ML systems for content recommendations, search, and spam detection, while augmenting user experience and system performance.

As an ML Infra Engineer, you'll build and maintain training and inference frameworks, scale distributed workloads, and improve system observability. You'll apply state-of-the-art methods while collaborating with ML and Platform teams.

The Senior Machine Learning Engineer will design, prototype, build AI systems, evaluate ML models, and improve Vectara's AI Agents with a focus on reliability and performance.

As a Senior AI/ML Software Engineer, you will design and deploy AI systems within a .NET environment, influence technical direction, and mentor engineers while integrating modern AI techniques.

As an ML Infra Engineer, you will optimize and scale training systems, manage GPU/TPU compute, and develop JAX training pipelines, collaborating with researchers for efficient model training.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

Design and implement model serving architectures, develop monitoring tools, and optimize performance for generative media models working with Applied ML teams.

The Staff Software Engineer will build and scale machine learning systems for personalization, collaborating with teams to enhance project quality and performance.

Design and develop scalable ML systems for healthcare, collaborating with teams to enhance products and maintain efficient infrastructures.

The Senior Machine Learning Research Engineer will develop and deploy deep learning pipelines, optimize model training, and collaborate with scientists to enhance AI/ML capabilities for cancer detection.

Design and develop core platform features for synthetic data generation, LLM operations, and agentic workflows. Oversee API design and scalable compute services, enhancing AI model performance for enterprise clients.

As a Senior Software Engineer, you'll build AI features focused on LLMs and RAG systems, collaborating across teams to ensure robust performance.

This role leads the ML Acceleration team, focusing on enhancing ML model training efficiency and team management, while driving technical strategy and collaboration.

Lead and manage a new ML Acceleration team, driving strategy and development for faster ML model training and optimizing performance.

The Senior Machine Learning Engineer will design and deploy AI systems, optimize production models, and ensure efficient performance in resource-constrained environments.

Design and build a customer-facing generative AI platform covering data generation, training, evaluation, serving, and agent-building. Own end-to-end product and backend distributed systems, ensure reliability, security, scalability, and collaborate with customers and ML researchers.

Lead end-to-end development of production ML systems for Uber Freight: identify opportunities, research algorithms (including deep learning and LLMs), prototype, deploy at scale, and monitor models. Integrate generative AI and agentic systems where valuable and collaborate with cross-functional teams to optimize logistics operations and network effects.

Lead research, design, prototype, and implement CV/ML/DL algorithms for defect detection, classification, metrology, and image analysis on semiconductor tools. Collaborate with cross-functional teams, benchmark and optimize algorithms, and productize solutions while considering compute constraints.

Optimize and deploy computer vision, signal processing, and ML algorithms to GPU-enabled embedded platforms. Profile CPU/GPU performance, reduce latency, containerize solutions, work in Linux, and collaborate with ML engineers to transition models into production on low SWaP systems.

Design, prototype, and operationalize ML models and data pipelines for temporal, geospatial, and track-based mission data. Build data processing, feature engineering, containerized cloud deployments (AWS/Docker/Kubernetes), APIs, and scalable evaluation/monitoring workflows while following Agile and software engineering best practices.

Develop and deploy deep learning computer-vision systems: source and curate data, design and train models (object detection), implement production-ready PyTorch/Python software, integrate with AWS, apply CI/CD and testing, and collaborate on research and technical solutions for intelligence-focused applications.

Develop backend systems and ML pipelines for computer-vision applications using Python, Docker, Kubernetes and AWS. Build RESTful APIs and streaming services, optimize performance, follow SDLC best practices, and collaborate in an Agile team to deploy containerized deep learning models.

Own the technical vision and evolution of Faire's ML platform: design scalable training/inference workflows, set MLOps standards, implement Unity Catalog and Databricks best practices, optimize cost and reliability, integrate LLM workflows, mentor engineers, and lead cross-functional initiatives to accelerate data science velocity.

Partner with customer platform and ML teams to design, deploy, and operate production AI/ML platforms on Kubernetes and public cloud. Lead implementations covering networking, security, observability, scaling, and automation; troubleshoot distributed systems; develop IaC, tooling, and reference architectures; and relay customer feedback to product and engineering.