Top Machine Learning Engineer Jobs

Build and productionize ML systems for a CTV ad-buying platform: write production Python, train/deploy/monitor models for real-time bidding and optimization, develop incrementality measurement and attribution, use LLMs/generative AI for internal tools, and serve as a technical lead and mentor on a distributed team.

Design, develop, test, and maintain cloud-native, API-driven AI/ML software solutions. Lead technical delivery, design scalable architectures, implement AWS-based deployments, productionize ML/GenAI applications, and collaborate with stakeholders to translate business needs into secure, maintainable systems.

Lead strategy, design, and delivery of decision intelligence and recommendation systems. Architect scalable model ecosystems, set ML best practices, ensure data privacy and observability, partner with product and engineering, and hire and mentor an engineering team to improve performance and user experience.

Design and build scalable backend services and data ingestion pipelines to process and enrich data (including LLM enrichment). Perform data modeling, mapping, and large-scale file manipulation while collaborating in an Agile team to deliver reusable APIs, optimize performance, and improve processes.

The Software Engineer will design and optimize large-scale data infrastructure, focusing on distributed systems and data processing for generative media models, collaborating closely with research scientists and enhancing multi-modal training pipelines.

The Senior ML Engineer will develop and deploy full-stack AI solutions, manage LLM integration, and collaborate closely with defense clients to enhance real-world applications in secure environments.

The role involves designing and maintaining large-scale ML infrastructure, optimizing distributed training systems, and enhancing computing performance for model training.

Design, train, and deploy state-of-the-art deep learning models (transformers, CNNs) for end-to-end navigation. Build perception, prediction, and planning solutions, implement data pipelines, deploy and monitor models in production, detect drift, and troubleshoot integration and performance issues for real-world robotic systems.

The role involves designing and maintaining machine learning models, analyzing data, and optimizing algorithms to solve business problems.

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

Design, train, and optimize high-performance ML/AI models and inference pipelines for remote sensor (OPIR) data. Integrate models into production Linux-based systems, collaborate with C++ and systems engineers, apply rigorous software engineering and Agile practices, and support missile warning, defense, and sensor-fusion applications.

Design and implement large-scale ML systems to optimize ad performance and supply personalization. Conduct data analysis, online experimentation, and causal inference. Build data pipelines and collaborate with engineers, data scientists, and product managers to drive product decisions and deliver scalable ad-serving solutions.

As a Senior MLOps Engineer, you will manage production ML systems, define lifecycle strategies, optimize ML pipelines, and collaborate with cross-functional teams to enhance ML operations.

Design and implement agent harnesses, evaluation benchmarks, and post-training pipelines. Develop RL environments and reward models, run and optimize multi-GPU training, profile and debug training jobs, and iterate on model fine-tuning to improve capabilities for agentic, edge-aware applications.

As a Staff Software Engineer in ML Platform at Stripe, you will lead complex projects, influence technical strategies, and mentor teams to enhance machine learning systems that power Stripe's products.

Deploy and maintain state-of-the-art ML models (LLMs and vision) in production for government customers. Improve models via retraining, tuning, and architecture updates; build scalable ML infrastructure; prototype ML-driven product enhancements; work with massive datasets and multimodal fine-tuning pipelines; collaborate across research, product, and engineering. Requires active security clearance and light travel (~10%).

Lead design and delivery of production-grade ML and Generative AI platforms. Define technical vision, own full SDLC, architect scalable distributed ML systems, partner with cross-functional teams to integrate and operationalize models (RAG, agents, LLM fine-tuning) at scale.

Optimize and deploy LLM inference systems across multi-node clusters to maximize throughput and minimize latency. Implement and benchmark inference optimizations, profile GPU and memory bottlenecks, build evaluation and benchmarking frameworks, and collaborate with research scientists to integrate new model architectures and techniques into production.

Lead SRE ownership of ML platform SLOs and operational health for Ray on EKS and Databricks on EC2. Maintain observability with CloudWatch/Datadog, tune autoscaling and GPU scheduling, manage Databricks workspaces and IAM, optimize cost/capacity, codify infrastructure with Terraform and CI/CD, lead incident response and postmortems, perform security/OS maintenance, and participate in on-call rotation.

Lead design and implementation of Metaflow to build, manage, and scale ML applications. Partner with ML researchers, OSS community, and cross-functional teams to improve ML platform reliability, CI/CD, observability, and developer velocity.

Design and build end-to-end tools and datasets for training and fine-tuning multi-modal generative models (video-focused). Implement large-scale batch inference and media data pipelines, contribute to storage/cataloging tooling, and partner with researchers, engineers, and product teams to operationalize Media ML at scale.

Design, implement, and evaluate alignment and quality metrics for member-facing LLM and recommendation systems. Build tools and pipelines for RLHF, reward modeling, fine-tuning, and human-in-the-loop evaluation. Collaborate cross-functionally, write production-quality code, and improve model robustness and metrics using member feedback and large-scale distributed systems.

Design and implement efficiency improvements for large-scale deep neural networks and LLM training/serving. Develop optimizations (quantization, pruning, distillation, efficient fine-tuning), work on ML hardware/software accelerator integration, and build scalable training and serving infrastructure in collaboration with scientists and cross-functional teams.

Build and scale web‑scale data pipelines and ML systems for Netflix's Knowledge Graph. Process structured and unstructured data, apply information extraction and graph ML, ensure data quality, and collaborate with ML scientists and business partners to deliver productionized data and ML products.

Design, build, and maintain developer productivity and reliability tooling for Netflix's machine learning teams. Collaborate with ML researchers to identify friction, implement solutions, and operate large CI/CD and distributed build systems to support large-scale data processing and model development workflows.