Top Machine Learning Engineer Jobs

Design and operate Kubernetes-based platforms for AI/ML systems, manage containerized environments, and develop CI/CD pipelines for secure deployment in mission-critical settings.

The GIS Software Engineer will develop and maintain GIS applications, focusing on AI/ML integration, UI systems, and troubleshooting, while supporting US Special Operations Command.

Develop, maintain, and enhance software systems focusing on data pipelines, machine learning analytics, and cloud technologies while integrating models within defense systems.

Design, train, and deploy threat-detection and anomaly-detection ML models; build and scale end-to-end pipelines on GCP (Spark, Dataflow, Dataproc, BigQuery, Vertex AI); maintain feature stores and production ML systems; collaborate cross-functionally; participate in on-call rotation and iterate on models serving enterprise customers.

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.

The ML Engineer will design and implement AI solutions for Axon's devices, collaborating with scientists to enhance model efficiency and ensure responsible AI deployment.

As a Software Engineer in the ML Products team, you'll maintain and enhance systems for Twitch discovery, partner with applied scientists on ML model improvements, and collaborate with product managers for new experiences.

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 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.

The role involves designing and refining reward signals for recommendations, collaborating with teams, and implementing ML solutions to enhance user satisfaction.

Develop scalable web applications using React and Next.js, implement backend solutions in Python, maintain DevOps practices, and collaborate with AI/ML teams to deploy applications.

The ML/LLM Engineer will optimize pipelines for ML and LLM systems, fine-tune models, collaborate on healthcare applications, and enhance model quality and safety.

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.

Interns will own ML platform projects to improve dataset creation, distributed training, and model evaluation pipelines. Tasks include profiling and benchmarking, enhancing observability, integrating MLOps tools, writing integration tests, and documenting and presenting results to improve reliability, scalability, and developer productivity.

Lead architecture and implementation of scalable ML data pipelines and tooling to turn autonomy data into training datasets. Build data selection, active learning, inference-at-scale, vector DB management, labeling integration, QC automation, and introspection tools while partnering with ML researchers and infra teams.

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.

The Research Engineer will improve dataset quality for self-driving systems by designing algorithms for auto-labeling and data monitoring, collaborating with teams, and enhancing model performance.

As a Senior Machine Learning Engineer, you'll develop and productionize ML models, design data pipelines, and ensure the scalability of features for over 200 million users, collaborating with various teams to enhance user experience.

Develop and maintain scalable ML infrastructure, collaborate with teams, and implement production ML systems while ensuring efficiency and reliability.

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

The Senior Machine Learning Engineer will automate healthcare compliance workflows using AI to process unstructured data, build multimodal AI pipelines, and develop extraction systems, ensuring high reliability and operational efficiency.

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

Intern will develop AI/ML applications for containerized production environments, perform model selection and training, prototype software architectures, research technologies, and support customers by debugging and collaborating with global ACS teams.

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.

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.