Top Machine Learning Engineer Jobs

Lead development and productionization of LLM quantization and deployment pipelines (PTQ, QAT, mixed-precision, INT8/FP4), build robust export/calibration/benchmarking/validation tooling, collaborate with research, field-testing, simulation and vehicle software teams, curate evaluation datasets and metrics, analyze numerical errors and performance trade-offs, and develop orchestration workflows for on-vehicle inference and model sign-off.

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.

The Data Scientist/ML Engineer designs and deploys Machine Learning algorithms, assists federal customers, and contributes to new feature design.

Seeking a Machine Learning Engineer to apply AI technologies in high frequency trading, focusing on automation and collaboration with AI research.

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.

Develop and deploy 2D traffic sign detection models for autonomous driving. Responsibilities include scenario analysis, dataset curation, model training and evaluation, optimization and quantization, building evaluation pipelines, long-tail data mining, and validating performance on edge/onboard platforms while collaborating across data, infrastructure, and deployment teams.

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.

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.

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

Design, train, and deploy reinforcement learning systems for high-impact decision problems. Build and evaluate RL policies at scale, handle reward modeling and simulation, and productionize stable, safe RL solutions.

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 role involves building and deploying AI-native data solutions, focusing on LLM and agent development, automation workflows, and data analysis, while leveraging cloud technologies.

Build systems for Mercor's hiring engine, including pipelines for indexing, sourcing, scoring, and conversion. Work with ML and backend technologies.

As a Machine Learning Engineer, you'll develop and deploy ML models for medical imaging, collaborate with teams, and optimize for production environments.

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.

Design, build, and deploy machine learning software and data pipelines for production government environments. Integrate and optimize distributed/cloud services, ensure code quality, lead reviews, mentor teammates, and engage directly with clients.

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.

The AI Research Scientist will develop and evaluate generative models for scientific data, collaborate with teams to apply AI in materials science, and mentor junior members while staying current in the field.

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.

Design, train, test, deploy, and maintain ML models for national-security cyber applications. Collaborate with software engineers and data scientists to build LLM-based automation, fine-tune open-source models, containerize solutions, and define ML strategy for mission-critical systems while meeting security requirements.

Design, train, test, deploy, and maintain ML models for defense customers. Collaborate with data engineers, scientists, architects, and product owners to implement deep learning, computer vision, and generative AI solutions and guide AI strategy for mission-critical systems.