Top Machine Learning Engineer Jobs

Design, train, evaluate, and productionize LLM-based NLU and agentic AI systems. Improve model quality, latency, reliability, and safety; fine-tune models, implement agent architectures, and collaborate across teams to deploy scalable, privacy-preserving conversational AI.

Lead design and implementation of agentic AI features for conversational and web UIs, collaborate with ML/GAI experts, PM, UX, and GTM teams, expand product reach and quality across customers and domains, identify patterns to improve the Agentic AI framework, and technically lead end-to-end delivery of scalable enterprise systems.

Lead design and scaling of a generative conversational engine and platform: build scalable APIs, optimize dialog performance and memory/latency, implement logging/tracing/metrics, enable integrations with chat clients, and collaborate with ML and product teams to deliver enterprise-grade agentic AI features.

Lead development and scaling of a generative conversational AI platform: build extensible infrastructure and APIs for chat clients, optimize dialog engine for performance and low memory, implement logging/tracing/metrics and debugging tools, and collaborate with ML and product teams to deliver domain-specific, multilingual enterprise experiences.

As a Machine Learning Engineer, you will enhance internal libraries for deep learning, collaborate with researchers, and optimize workflows for cost savings.

The role involves designing and deploying AI systems for vaccine discovery, leading predictive modeling, integrating datasets, and mentoring colleagues on AI applications in vaccinology.

As a Staff Applied AI Scientist, you will design and deploy AI systems using LLMs, improve agent performance, and collaborate with engineering to enhance systems for customer impact.

Lead vision and roadmap for large-scale recommender systems across Spotlight, Discover, and Friend Stories. Technically lead engineering teams, design next‑generation recommender and retrieval architectures, incorporate SOTA research and foundation models, advance ML infrastructure for scalability and reliability, and partner cross-functionally to drive experimentation, operational excellence, and cost-effective production deployments.

Design, implement, and deploy ML inference engines on custom FPGA/ASIC hardware. Perform HW/SW co-design with traders and researchers, optimize neural networks for low latency, and build quantization/compression tools to translate ML frameworks to RTL for rapid production deployment.

Design and implement generative ML systems (image, video, audio, multimodal LLMs) and deliver on-device and server-side inference. Build GenAI pipelines and AR experiences, prototype with cross-functional teams, and optimize efficient models for real-time mobile and wearable applications.

As a Principal AI/ML Engineer, you will lead the development of machine learning solutions for advertising, focusing on optimization, personalization, and deploying models within a highly scalable infrastructure.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

The Principal AI/ML Engineer will develop advanced ML models for the AdTech platform, lead system design, and collaborate across teams to optimize advertising strategies using AI.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Senior individual contributor designing and deploying onboard ML solutions for autonomous driving. Develop models (detection, tracking, trajectory, safe AI), build training pipelines and deployment tooling, translate research into production, collaborate cross-functionally, and mentor engineers.

Design, implement, and deploy ML models for autonomous driving (detection, tracking, trajectory generation). Apply advanced techniques (imitation learning, RL, foundation models), collaborate cross-functionally, and support production ML systems and model deployment.

The Staff Machine Learning Engineer will lead the development of search ranking systems, focusing on machine learning models for relevance, quality, and personalization through various advanced techniques and collaboration with cross-functional teams.

Drive technical strategy for the Personalization Platform, develop recommendation systems, maintain ML workflows, and innovate AI solutions for customer engagement.

Lead end-to-end ML initiatives: design, prototype, optimize, and productionize models (especially LLMs and generative models) on healthcare data; run A/B and statistical analyses; research novel ML approaches; mentor and recruit ML staff; present to leadership and drive optimization initiatives.

The Principal AI/ML Engineer will develop AI/ML solutions for hardware and firmware validation, enhance test efficiency, and collaborate across teams to implement data-driven analytics and automation.

Lead the design, development, and deployment of AI and machine learning solutions, mentor teams, and drive innovations in healthcare data analysis.

Design and implement AI/ML solutions for healthcare, collaborating with teams to translate complex requirements, deploy models, and process large datasets.

The Senior AI ML Engineer will develop AI systems, ensure model quality, lead architecture governance, and manage observability for healthcare solutions.

Lead the design and development of AI/ML models for healthcare. Collaborate with business partners and other teams to ensure high-quality execution and adherence to architectural principles.

Lead design and development of scalable AI architectures and frameworks, collaborate with research and product teams, deploy and optimize ML/LLM systems in production, guide ML platform strategy, perform code reviews, mentor engineers, and evaluate emerging AI technologies for business impact.