Top Machine Learning Engineer Jobs

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

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

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

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

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

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.

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.

Build and maintain scalable backend services, APIs, and data pipelines for computer vision and LLM workflows. Integrate model serving, ensure reliability, performance, and observability, collaborate with ML and Solutions Engineering, and support production deployments and occasional on-site testing.

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.

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.

The Senior Machine Learning Engineer will design, build, and deploy scalable ML systems, collaborating closely with teams to optimize AI processes and mentoring juniors.

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.

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.

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

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.

Lead design, build, and modernization of recommendation systems integrating backend engineering, data pipelines, and LLMs. Develop, test, deploy, and maintain scalable microservices and MLOps solutions, mentor engineers, collaborate with product teams, and drive system design, monitoring, and retraining pipelines for production ML.

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.

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.

Design, build, and maintain MLOps pipelines and infrastructure for Traditional, Generative, and Agentic AI. Implement CI/CD, containerized deployments (OpenShift/Docker/Kubernetes), cloud integration (AWS), automation (Bash/Python), logging/observability, security controls, and collaborate with data scientists and DevOps to deploy and optimize production AI systems.

Lead design and implementation of cloud-native, microservices backend systems that power AI-enabled features (LLM assistants, RAG pipelines, recommendations). Build scalable REST and event-driven APIs, establish MLOps practices, ensure reliability/security/observability, mentor engineers, and collaborate with product, scientific, and infrastructure teams to productionize AI services in regulated scientific environments.

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.

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

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.