Top Machine Learning Engineer Jobs

Build and maintain ML platform tooling and pipelines for perception, prediction, and planning. Enable dataset curation, distributed training, evaluation, and deployment (cloud and edge). Optimize tooling, collaborate with ML teams, review code, and drive long-term platform strategy while working in a hybrid Palo Alto environment.

Define long-term architecture and technical strategy for Target's enterprise ML/Ops platform. Design cloud-native, scalable, secure Kubernetes-based platforms; establish ML lifecycle, deployment, governance, observability, and responsible AI standards; evaluate technologies; mentor engineers; and partner with infrastructure, security, and product teams to modernize platform capabilities.

Build and productionize large-scale ML models and pipelines for query understanding and personalization. Work on autocomplete, query tagging/NER, query expansion, intent modeling, and LLM-powered natural-language search. Collaborate cross-functionally to design, deploy, and monitor batch and real-time systems, leveraging modern ML infrastructure and tooling to improve search relevance across Airbnb products.

Architect and develop a high-performance ML runtime for autonomous vehicles, optimizing for both onboard and offboard systems while collaborating with Waymo ML practitioners and enhancing tooling for performance analysis.

Design, fine-tune, evaluate, and deploy LLM-driven, user-facing browser features using RAG, embeddings, prompt engineering, and modern ML tooling. Lead end-to-end model lifecycle, run production experiments, collaborate with product and engineering, and document and review work with an emphasis on privacy, latency, and usability.

Lead R&D on LLM training and inference optimization, train and evaluate large-scale models on Tenstorrent hardware, improve performance via techniques like speculative decoding, quantization, kernel fusion, flash attention and distributed training, diagnose system bottlenecks, and translate research into scalable production solutions through cross-functional collaboration.

Lead MLOps for production ML lifecycle: deploy and serve models, build scalable reproducible pipelines for large protein-design workloads, implement CI/CD and observability, optimize GPU/accelerated compute, and enable self-service tooling for researchers.

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

Design and optimize high-performance ML-focused ASIC microarchitectures. Translate researcher requirements into RTL, specify execution units and datapath elements, perform power/area/performance tradeoffs, and collaborate with verification teams to ensure functional correctness and target ML performance.

Build and operate a production ML platform for low-latency, high-availability model inference: own deployment infrastructure, CI/CD, observability and drift detection, partnered handoffs with Data Science, and experimentation/canary routing and explainability outputs.

As an MLOps engineer, you will develop optimized data pipelines, enhance data engine architecture, and integrate foundation models while working closely with DoD customers.

Lead design and build of scalable batch and streaming data pipelines for multimodal and spatial AI. Enable dataset curation, versioning, lineage, and data quality for research-to-production ML workflows. Partner with researchers and architects, run proofs‑of‑concept, optimize cost and performance, and mentor engineers to maintain observable, reproducible AI data systems.

Designs, deploys, monitors, and scales machine learning models and infrastructure. Builds CI/CD pipelines for training/deployment, manages ML resources, implements model monitoring, curates clinical datasets, and collaborates with data scientists and clinicians to optimize model performance.

Develop algorithms and optimizations for inference and compiler stack, enhance performance metrics, collaborate with hardware teams, and publish work on novel approaches.

Design, develop, train, evaluate, and deploy machine learning models using large datasets. Integrate ML/AI solutions into production systems, perform data preprocessing, model selection, cross-validation, and optimize model performance while supporting system architecture and engineering tasks.

Build and maintain ML and generative AI tooling, training and inference platform services, model evaluation and testing frameworks, and APIs to expose ML products. Deploy and manage applications across the AI/ML SDLC, mentor teammates, and write production-quality code daily.

Design, implement, and optimize BEV-based perception models that fuse camera, LiDAR, and radar inputs; benchmark models on large-scale datasets; collaborate with research, data, and deployment teams to ensure performance, robustness, and scalability; and maintain QMS compliance and quality/process improvements for production deployment.

Design, build, and operate large-scale, low-latency recommendation systems (retrieval, ranking, multi-objective optimization). Drive offline training, online inference, A/B testing, and metric analysis. Apply LLMs and foundation models for content understanding, user intent, and feature generation. Rapidly prototype AI/LLM ideas and own projects from MVP to production, ensuring data quality and system performance.

Develop and optimize generative AI applications and traditional ML models for advertising. Lead innovations, mentor engineers, and ensure system robustness.

Lead development and productionization of personalization and recommendation algorithms (ranking, retrieval, content/user understanding, post-ranking). Apply modern AI/LLM techniques, design and run offline and online experiments, drive technical vision and roadmap, collaborate with engineering/product/data teams, and build maintainable, production-grade ML systems at scale.

Lead the development of machine learning algorithms for personalization and recommendations in Disney's streaming products while collaborating with product and engineering teams.

The role involves leading technical initiatives in machine learning infrastructure, developing data pipelines, and collaborating across teams to enhance personalized news content delivery.

Founding ML infrastructure engineer to design and scale a GPU compute platform (1,000+ GPU clusters), build multi-tenant scheduling and marketplace infrastructure, own production reliability, architect feature stores/model registries and experiment tracking, and develop resource orchestration and scheduling to optimize throughput, cost, and latency.

Founding ML Performance Engineer will write custom CUDA kernels, optimize end-to-end model inference for sub-50ms latency, drive 10x stack performance (memory bandwidth, kernel fusion, scheduling), implement zero-copy multi-GPU/multi-node memory, own GPU utilization and memory management, profile and benchmark pipelines, and build performance tooling and standards.

Build and scale backend systems and cloud-native infrastructure for large-scale ML workloads. Implement distributed training/inference pipelines, developer tools, and observability for GPU-heavy jobs while collaborating with ML engineers.