Top Machine Learning Engineer Jobs

The Lead AI Engineer will manage AI/ML model production, ensure deployment pipelines, mentor teams, and enhance AI systems with innovative technologies.

The role involves designing, developing, and deploying machine learning models, especially in NLP, optimizing inference performance, and collaborating with cross-functional teams to enhance solutions.

Design, build, and scale an end-to-end AI/ML platform and MLOps capabilities. Enable model serving, deployment, and monitoring; guide junior engineers; deliver production-ready platform solutions across the SDLC with minimal supervision.

As a Staff Software Engineer at PayPal, you will lead the design and scaling of AI/ML platform components and MLOps capabilities, focusing on technical leadership and building production-grade platforms. You will coordinate teams, improve processes, and ensure the technical integrity and innovation of the AI/ML infrastructure.

As an ML Platform Engineer, you will enhance the core ML platform's performance and scalability, enabling faster innovation and impactful machine learning solutions.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving AI solutions.

Lead design, development, and deployment of advanced machine learning models and infrastructure. Own architecture, data pipelines, experimentation, validation, and team technical leadership to drive AI-driven product features and business value.

Design, develop, deploy, and improve production ML and computer vision models; evaluate model performance; support MLOps/CI-CD workflows; mentor junior engineers; collaborate across product and engineering teams.

Design, implement, deploy, and monitor production-grade ML and computer vision models (CNNs, transformers, vision-language). Evaluate models, analyze data, improve robustness, support MLOps/CI-CD, and mentor junior engineers within an Agile team.

As a Senior Machine Learning Engineer, you'll lead the development of a neural welding simulator, integrating research and production to enhance robotic systems. Responsibilities include building predictive models, collaborating with reinforcement learning engineers, and translating prototypes into maintainable production code.

The role involves developing AI/ML prototypes, collaborating with customers, database administration, and enhancing machine learning model capabilities, requiring extensive experience and specific technologies.

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 develop scalable data pipelines and storage systems for machine learning evaluation, and create tools for data annotation and monitoring.

This role involves developing and deploying large language models and generative AI systems, building scalable ML infrastructure, and mentoring junior engineers while collaborating with cross-functional teams.

Lead and own the data science function: design and build production ML systems (recommendation, forecasting, segmentation), develop configurable multi-tenant models, run rigorous experimentation and causal analyses, connect model outputs to business impact, set scientific standards, and hire and mentor a high-velocity data science team.

Design and implement quantum-inspired and quantum machine learning models (tensor-network architectures), build hybrid classical-quantum workflows, collaborate with hardware and experimental teams, optimize models for HPC/GPU and low-SWaP deployment, contribute to research publications and technical reports, and provide mentorship in an interdisciplinary R&D environment.

Optimize large-scale ML training and inference workloads by profiling bottlenecks, implementing efficiency improvements (parallelism, mixed precision, compilation), building observability and benchmarking tools, and collaborating with research and platform teams to scale models and increase training throughput.

The role involves leading compiler engineering, innovating compiler infrastructure and optimization algorithms for ML performance, and working with various teams to implement solutions.

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.

Optimize machine learning models for embedded environments, focusing on performance, efficiency, and deployment strategies across various platforms.

Senior AI/ML Research Engineer to design and implement ML algorithms and large-scale data pipelines, deploy models on cloud infrastructure, and establish ML governance practices. Apply advanced mathematics, signal processing, and reproducible research to solve RF and time-series problems in a cleared environment.

Develop and optimize AI systems by designing components, maintaining datasets, collaborating with teams, and ensuring agent performance. Drive outcomes in a dynamic environment.

Design and own AI/ML systems, coordinating with global teams, and overseeing production software for effective deployment and operation.

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.

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.