Top Machine Learning Engineer Jobs

Develop, debug, and maintain AI applications and tools for large-scale data analysis. Collaborate with engineers and leadership to design solutions, document procedures, and support users.

Lead development of spatial deep learning models (diffusion and 3D generative) from data curation through training, evaluation, and production deployment; create scalable training datasets from LiDAR, images, videos, and floorplans; combine geometry algorithms with learned models and integrate outputs into an interactive 3D product stack.

The Senior ML Infrastructure Engineer will own data infrastructure, maintaining pipelines for underwriting and claims, and partner with data scientists to implement business logic into production code.

Develop and deploy advanced ML solutions for OCR and NLP, optimizing models for performance and integrating them into production workflows while collaborating with cross-functional teams.

Join the SOTA Training Platform team to bring up ML models on Cerebras systems, enhancing performance across the software stack and debugging issues.

The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.

The role involves designing and developing backend systems and applied machine learning solutions for integrity and safety challenges in AI interactions. Responsibilities include leading technical solutions, collaborating with cross-functional teams, and mentoring engineers while establishing best practices in software engineering and machine learning.

Design and optimize pre-training pipelines for VLM models, conduct research on pre-training techniques, and evaluate performance for autonomous driving.

Design and implement compiler optimizations and new compiler features for a high-performance neural network inference platform. Analyze generated code performance, collaborate with hardware architects for hardware/software codesign, and work with model developers to tune networks for inference efficiency and accuracy. Leverage agentic AI to accelerate analysis, planning, and production software improvements.

The ML & Cloud Infrastructure Engineer will develop scalable AI and data pipelines, deploy monitoring and CI/CD pipelines, and optimize performance for cloud-based ML applications.

The role involves designing ML pipelines for computer vision, optimizing models for edge deployment, and developing data management systems for sensor datasets.

The ML Research Engineer will implement and deploy deep-learning models for perception AI, manage data pipelines, and design user interfaces.

Design, develop, and implement ML pipelines and collaborate with teams to integrate models, optimize techniques, mentor engineers, and communicate effectively.

Design and improve ML training frameworks, modularize experiments, adapt fire-hazard detection algorithms for varied electrical environments, build scalable pipelines for high-frequency sensor data, and develop dashboards and tooling to monitor model performance and drift while collaborating across product, data science, and fire safety teams.

Design, build, and run large-scale backend and data infrastructure; productionize and run experiments on AI/ML models; implement MLOps best practices (versioning, monitoring, alerting); improve model accuracy, speed, and cost; and drive engineering process and culture improvements.

The Senior Machine Learning Engineer will design, train, and evaluate ML models to solve complex problems, mentor others, and optimize production systems.

Develop, design, and maintain AI/ML software solutions; collaborate with engineers and leadership; build tools to analyze large-scale data; debug, profile, optimize, and test software; create documentation and user support materials; leverage modern LLM tools to accelerate development.

Design, build, and productionize ML models; maintain data pipelines; collaborate with teams to deliver AI features and enhance product experiences.

Develop and maintain C++ machine control and data analysis software for advanced mask inspection systems. Collaborate with multidisciplinary teams to define system requirements, optimize performance, support integration and testing, and resolve customer escalations. Work in Linux environments and leverage RPC/REST APIs, containerization, and modern UI frameworks as needed.

Develop and optimize multi-modal (Lidar, Camera, Radar) 3D occupancy, segmentation, and flow models. Architect temporal and vision-first fusion approaches, ensure temporal consistency, and optimize networks for real-time on-vehicle inference. Collaborate with tracking, prediction, and planning teams to deliver precise geometric and motion outputs for autonomous navigation.

The Senior AI/ML Engineer will develop and enhance AI/ML models using Python, specializing in OPIR domain expertise, with a focus on object identification and classification.

The ML Engineer will develop and deploy machine learning models, collaborate with teams, analyze datasets, and improve model performance.

Build and operate large-scale ML training infrastructure: distributed GPU training, multi-cluster scheduling, data pipelines (batch and streaming), ML workflows, observability, reliability, and on-call incident response to keep autonomy model training running efficiently.

Build and operate large-scale ML training infrastructure: distributed GPU training, multi-cluster scheduling, data pipelines (batch & streaming), ML workflows, observability, alerting, and on-call/incident response to ensure reliable, cost-effective model training and releases.

Design, build, and maintain ML infrastructure for model lifecycle: training, optimization, validation, deployment, inference, and observability. Own ML inference and compiler platforms, develop scalable ML pipelines, continuous testing/monitoring, and support deployment to diverse hardware for autonomous vehicles.