Top Machine Learning Engineer Jobs

Develop and implement reinforcement and supervised learning algorithms for robotic manipulation, build simulations and domain randomization, optimize model architectures and data pipelines for sample efficiency, and deploy, evaluate, and debug policies on physical robots.

Develop and train computer vision and visual representation pipelines for robotic manipulation (RGB, RGB-D, depth, segmentation, pose, keypoints). Build perception models to support RL and imitation policies, improve synthetic/real data pipelines, evaluate models on real robots, calibrate sensors, and collaborate with robotics and simulation engineers to iterate on models and data for robust deployment.

Design, implement, and maintain large-scale ML/RL training infrastructure: job orchestration, scheduling, checkpointing, experiment tracking, developer tooling, distributed training, and resource management for cloud compute.

Join the AI team to build automation features in AI/ML, owning projects from concept to integration, and collaborating with leadership.

Design, build, and maintain MLOps software systems and ML pipelines; deploy, test, and monitor AI/ML models on cloud-native platforms; implement ETL and data workflows; ensure model performance, security, and scalability; lead technical roadmap, mentor engineers, and evaluate emerging MLOps tools.

As an ML Infra Engineer, you will optimize and manage training systems for large-scale models, collaborating with researchers and engineers on infrastructure and experiment scalability.

As a Senior Machine Learning Engineer (Tech Lead), you'll lead the ML technical direction, design architectures for loco-manipulation, mentor team members, and manage cross-functional partnerships while writing production-quality code.

The ML Systems Engineer will design and manage efficient training and inference systems, optimize hardware utilization, and collaborate with researchers on RL loop integration, enhancing scientific discovery.

The role involves deploying and optimizing LLMs, designing the ML serving stack, and ensuring high-performance GPU services for production readiness.

Build and maintain ML infrastructure for robot manipulation, including data pipelines, training systems, and real-time inference, collaborating with researchers.

The Senior Software Engineer will build foundational AI infrastructure, design platforms and tools for AI applications, ensure reliability, and collaborate with AI teams to support production cases.

Design and oversee the development of robust data platforms for training and evaluating ML models, focusing on autonomous driving. Collaborate with multiple teams and optimize data processing pipelines.

The Senior ML Engineer will automate component placement on PCBs, working on R&D and production using ML and optimization techniques.

As a Senior/Staff ML Systems Engineer at Quilter, you'll design and maintain ML infrastructure for automated PCB component placement, manage data pipelines, and lead systems design reviews while ensuring project efficiency and reliability within a fully distributed team.

Design and build production agentic systems for short-form video: architect context gathering, planning and tool selection; integrate state-of-the-art models; measure and iterate on agent quality using evaluation frameworks and real-world feedback; own end-to-end deployment balancing performance, reliability, and scalability.

The role involves owning and delivering AI-powered features for Calendly, collaborating with multiple teams, and applying machine learning solutions for product enhancement.

Build and operate high-throughput experimentation and recommendation infrastructure. Design APIs and services, use agentic AI workflows (LLMs) for development, ensure performance and reliability, participate in on-call, mentor teammates, and codify AI-native engineering practices.

Design and build ML infrastructure and models for ad performance: feature engineering, batch and real-time training and serving, low-latency evaluation, production performance optimization, cross-functional collaboration, and monitoring model quality at large scale.

Design, build, and operate large-scale ML systems for ad performance: feature engineering, model training/deployment (batch and real-time), evaluation, monitoring, and production optimization while collaborating with cross-functional teams.

Design, develop, and maintain scalable data products and MLOps infrastructure. Build and fine-tune ML and deep learning models (including LLMs, NLP, and vision-language), monitor model performance, ensure data security and compliance, collaborate with cross-functional teams, document pipelines, and introduce improvements and new tools.

Design, build, and operate secure, scalable cloud-native backend systems for unstructured data and AI workloads. Partner with platform, security, and ML teams to create microservices, APIs, and data pipelines, deploy large-scale ML models, and deliver self-service capabilities while prioritizing privacy, ethics, and reliability.

Collaborate to scale self-driving systems, build training frameworks, profile model performance, and evaluate emerging technologies for training and inference.

Lead the development of AI/ML infrastructure for simulations. Collaborate on realism models, scale distributed systems, and ensure alignment with business goals.

As a Senior Software Engineer in Robotics, you will transition Perception tasks to machine learning, focusing on system integration and optimization.

Lead the Inference ML team in developing tools and APIs for large-scale ML applications, enhancing performance and usability, while collaborating across engineering teams.