Top Machine Learning Engineer Jobs

Develop and productionize AI features from PoC to scalable deployments. Write production-ready Python code, implement testing and debugging, refine user stories, participate in ethical and security reviews, and collaborate with stakeholders to align AI solutions with business needs.

Design, train, evaluate, deploy, and monitor machine learning models. Prepare and analyze large datasets, improve model performance through experiments, and collaborate with software engineers to integrate ML solutions into production while staying current with ML techniques.

Design, build, deploy, and operate AI/ML capabilities in AWS GovCloud using FedRAMP-authorized services. Responsibilities include data preparation, model development, evaluation, deployment, monitoring, RAG implementations, embedding/vector search integration, Python development of production services, containerized deployments, CI/CD, and collaboration with engineering and cybersecurity teams to meet mission and compliance requirements.

The Senior Staff Machine Learning Engineer will lead innovative AI engineering projects and optimize algorithms for e-commerce applications, collaborating across teams to drive significant business impact.

Support climate and Earth system researchers by developing, deploying, and optimizing AI/ML workflows on HPC and GPU-accelerated systems. Assist with compiling, profiling, tuning, and porting scientific applications; optimize system utilization (CPU/GPU, memory, storage, I/O); maintain scientific software environments and datasets; provide consulting, training, documentation, and grant support to advance AI-enabled climate research.

Design, implement, and optimize ML and deep learning models; preprocess and prepare large datasets; integrate models into production; monitor and maintain model performance; collaborate with engineers and stakeholders; stay current on ML/AI advances.

Design, implement, and optimize machine learning and deep learning models; preprocess and analyze large datasets; integrate models into production; monitor performance and troubleshoot; collaborate with engineers, data scientists, and stakeholders; stay current with ML/AI advances.

Own and build end-to-end matching and ranking systems for out-of-home advertising: design candidate generation, re-ranking, geospatial scoring, data warehouse and feature pipelines, evaluation infrastructure, low-latency ranking APIs, and model monitoring with drift detection and retraining triggers.

Design and deploy scalable production ML systems powering Diffbot's Knowledge Graph. Improve model accuracy via research, feature engineering, and hyperparameter tuning; own end-to-end performance for 60+ ML problems; assess new research for production feasibility; collaborate on training data and error analysis with product and customers.

Build and deploy low-latency, production-grade ML models and end-to-end pipelines for fraud detection. Engineer high-frequency time-series features from massive event streams, maintain automated MLOps/CI-CD infrastructure, optimize runtime scoring performance across distributed systems, and collaborate with infrastructure, product, and data science teams to translate fraud signals into robust algorithmic solutions.

As an ML research engineer, you will develop systems using language models, curate datasets, set evaluation metrics, and scale semantic search processes.

Lead architecture and maintenance of end-to-end traffic classification ML systems for adversarial domains. Own model lifecycle from offline evaluation to millisecond-edge deployment, build certified offline-to-online data pipelines, establish rigorous evaluation and leakage-prevention, optimize inference under strict latency/cost budgets, integrate scoring with mitigation workflows, and provide cross-functional leadership and mentorship.

Design and scale data pipelines and automated closed-loop systems for continuous improvement of perception models. Deploy active learning, curate and prune large datasets, develop evaluation metrics and ground-truth‑free performance measures, and build robust mining, training, and evaluation pipelines for rare, safety-critical scenarios while collaborating with research and operations teams.

As a Generative AI & Machine Learning Engineer, you'll manage AI/ML product development, support model lifecycle, and ensure solutions meet customer needs, fostering collaboration and innovation.

Design and deploy scalable ML infrastructure, implement CI/CD for models, monitor production models, build generative AI solutions, ensure security/compliance, preprocess/analyze data, develop and fine-tune models (TensorFlow/PyTorch), and communicate results to stakeholders. Onsite presence may be required.

Lead design, training, and deployment of custom deep-learning models for Radar and EO/IR semantic segmentation, detection, and temporal tracking. Build multi-stage pipelines, train on large proprietary datasets, optimize C++ inference for resource-constrained ARM64/CPU edges, advance sensor-fusion with AIS and cartography, integrate ML outputs into multi-target tracking/SLAM, and contribute to MLOps for continuous training and evaluation.

You will design and develop machine learning systems for an AI-driven dating platform, focusing on personalized matching algorithms and natural language interactions.

As a Machine Learning Engineer, you will design and train perception models for robotics, focusing on 3D perception systems. You will tackle complex physical challenges, optimize models for embedded hardware, and collaborate with cross-functional teams.

Develop and scale learning models for behavior cloning and reinforcement learning. Build data pipelines and evaluate model performance for integration into autonomous systems.

The Machine Learning Engineer will design, implement, and optimize reinforcement learning models for autonomous driving systems, enhancing data systems for scalable evaluation and performance assessment.

Design, train, and deploy multimodal machine learning systems that fuse 3D/vision, simulation, tabular, and text data. Own end-to-end model lifecycle from architecture and training to evaluation, production deployment, and monitoring, and build agentic workflows and retrieval/embedding capabilities to support underwriting and mitigation decisions.

Lead design and delivery of large-scale ML and AI systems for Airbnb Community Support products. Develop and productionize LLM-based solutions (fine-tuning, RAG/search, evaluation automation, feedback learning, guardrails), collaborate cross-functionally with product and design, and shape early-stage AI initiatives through to production to improve customer support experiences.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

This role involves optimizing inference performance, managing high-performance systems, scaling models, and full-cycle ownership from research to production in a fast-paced environment.

As an Open-Source Machine Learning Engineer at Hugging Face, you'll improve ML libraries, work with the community, and contribute to open-source projects.