Top Machine Learning Engineer Jobs

Build and consolidate data infrastructure for ML training: design automated ETL/ELT pipelines, implement DataOps practices (validation, monitoring, anomaly detection), integrate annotation workflows, manage dataset versioning and storage, and collaborate with ML engineers and operations to produce high-quality, reproducible training datasets.

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.

Design and operate ML training and serving infrastructure, build a Kubernetes+Ray backend, improve developer experience and observability, mentor engineers, and ensure reliable, secure deployments for low-latency, high-performance systems.

Build and harden low-latency, high-throughput ML platform infrastructure. Design storage, indexing, streaming, backpressure, failure handling, and observability. Scale decisioning, improve developer experience for data scientists, manage Kubernetes deployments, and collaborate with SRE, data infra, and engineering to ensure reliable, debuggable production systems.

As a Senior Machine Learning Engineer, you will develop advanced machine learning and deep learning models and platforms for optimizing advertising performance and conduct complex experiments.

Design, operate, and scale cloud-native ML infrastructure across GCP and AWS (GPU/TPU), build CI/CD for models, maintain low-latency real-time inference systems, define observability and monitoring for ML models, participate in on-call incident response, and partner with data scientists to improve MLOps and platform usability.

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.

Develop and optimize lightweight machine learning models for edge AI applications, integrating into production systems and mentoring junior engineers.

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

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.

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

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.

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.

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.

Design, develop, and maintain scalable AI/ML systems and end-to-end pipelines (data preprocessing, model training, prompt engineering). Collaborate cross-functionally, optimize model performance, ensure fairness and security, monitor production systems, troubleshoot issues, mentor junior engineers, and recommend improvements to data infrastructure and tooling.

Lead Responsible AI projects to detect, mitigate, and evaluate bias across GenAI, foundation models, and recommender systems. Collaborate with product and platform teams, deploy fairness interventions at scale, mentor junior engineers, and define technical strategy to align models with Pinterest policies and business metrics.

Develop and prototype large-scale text-to-image generative models (Pinterest Canvas), collaborate on multimodal embeddings, collect and prepare visual training data (including RLHF and fine-tuning), ship models to production, publish research, and mentor junior researchers.

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.

Lead and build an ML Acceleration team to define strategy and implement scalable solutions to speed ML training. Hands-on role combining technical design (distributed training, hardware and model optimization) with people management, cross-functional collaboration, impact measurement, and continuous performance improvements for production autonomous vehicle ML systems.

The role involves designing and optimizing recommendation systems for Fortnite's Discover experience, including model implementation and A/B testing.

Lead end-to-end ML projects for fraud detection: develop, deploy, monitor, and iterate production models and experimentation frameworks. Improve model performance, build scalable ML pipelines, collaborate with Product/Engineering/Risk, and champion testing, documentation, and model-monitoring best practices to ensure robust, high-impact solutions.

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

Design and optimize recommendation systems using deep learning approaches, manage pipelines and evaluate A/B tests to improve user experiences.