Top Machine Learning Engineer Jobs

Drive end-to-end ML model inference performance: build kernel- and system-level performance models, optimize kernel microcode and compiler algorithms, debug runtime performance on system and cluster, and develop tooling to visualize and analyze performance data from the Wafer Scale Engine and compute cluster.

Design and build reinforcement-learning training environments and diverse tasks to evaluate and improve LLM agents; iterate rapidly on task designs from customer feedback, deliver high-quality outputs with minimal supervision, and maintain PST overlap for collaboration.

Develop and maintain high-performance C++ machine control and data analysis software for advanced mask inspection systems. Integrate AI/ML capabilities, collaborate with multidisciplinary engineering teams, optimize and scale existing code, and support integration/testing and customer escalations. Work with APIs, containers, and observability tools to enable reliable production deployment.

Design, build, and deploy production ML systems that combine structured clinical/EHR data with computer vision outputs. Own model calibration, interpretability, monitoring, drift detection, retraining workflows, and regulatory documentation. Partner cross-functionally, mentor engineers, and translate clinical questions into reliable, production-grade ML solutions.

Design, build, deploy, and operate production-grade ML systems and pipelines for a massive consumer audience. Own full lifecycle from data processing and feature engineering through training, validation, deployment, and monitoring. Drive ML infrastructure and architecture decisions, partner with product and data teams, optimize models against business metrics, and mentor engineers to ensure security, reliability, and compliance.

Build and operate Hadrian's production ML platform: standardize MLflow/Dagster-based deployments, serve batch and online inference, maintain feature serving and lineage, detect drift, enable model CI/CD, observability, autoscaling, and developer tooling for reliable model production across factories.

The Research Engineer, Infrastructure will build distributed training systems, optimize performance, manage data pipelines, and enhance research workflows, ensuring the infrastructure scales with AI advancements.

Build and operate model validation, quantization, and safety systems for production ML. Develop pipelines for security scanning, optimization (quantization/pruning), routing, prompt management, and evaluation frameworks measuring accuracy, performance, and safety across model lifecycles.

Develop, deploy, and support secure, scalable production software and AI/ML solutions. Collaborate with product, UX, and engineering to deliver features, create tests and automation, instrument monitoring and dashboards, and participate in agile processes while continuously learning and improving team practices.

Design, build, and maintain scalable MLOps pipelines and data workflows to train, fine-tune, deploy, and monitor multilingual text and speech translation models. Prepare and optimize multilingual datasets, support model evaluation and benchmarking, and enable low-resource language training while collaborating with data scientists to integrate models into production.

Design, build, and operate core AI capabilities and reusable model integrations for an enterprise platform. Integrate foundation models into production, optimize performance and reliability, prototype new techniques, and support scalable backend services and distributed AI systems in collaboration with cross-functional teams.

Design, deploy, and productize scalable traditional and generative AI/ML solutions. Collaborate with data scientists and engineers, build LLM-based agents, ensure code quality and reliable CI/CD deployments, mentor team members, and drive adoption of state-of-the-art ML techniques.

Develop and prototype AI/ML-driven automation for ASIC/SoC design and implementation. Collaborate with academia and internal teams to apply GenAI, RL, GNNs, and CNNs to VLSI CAD flows, pilot solutions on real designs, and improve power, performance, area, and quality.

As a Senior Software Engineer at NVIDIA, you will design and implement inference software optimizations for AI applications using TensorRT, collaborating with deep learning experts to influence hardware and software design.

This role involves designing, developing, and maintaining AI/ML products, collaborating with stakeholders, and ensuring model deployment on AWS. Responsibilities include creating APIs, establishing MLOps practices, and driving AI strategy for operational improvements.

Build scalable agentic frameworks and reproducible experimental pipelines that integrate frontier multimodal models with rich neuroscience data. Implement orchestration, MLOps, and cloud infrastructure, balancing research and production engineering on a small collaborative team.

Optimize training and inference performance of foundation models by writing and tuning CUDA/Triton GPU kernels, profiling and removing bottlenecks, implementing low-precision and mixed-precision strategies, optimizing MoE routing and expert dispatch, integrating high-performance libraries, and collaborating closely with researchers to translate model ideas into scalable, efficient implementations.

Design and build an end-to-end, high-throughput dataloading stack for massive multimodal datasets: formatting, preprocessing, filtering, sharding, caching, and streaming data to distributed GPU training with observability, reliability, and performance benchmarking.

Design, build, and optimize distributed training systems for foundation models across thousands of GPUs. Implement advanced parallelism, fault-tolerance, profiling, and tooling to enable large-scale, reproducible ML experiments in cloud/HPC environments.

Develop, scale, and productionize state-of-the-art computer vision foundation models for multimodal data pipelines. Move research code to production, optimize inference and memory, build reliable ML workflows, and collaborate closely with researchers and engineers on large-scale AI research.

Design, build, and scale ML infrastructure: ingest vehicle sensor data, create batch pipelines for dataset curation, run distributed GPU training, and ensure performance, observability, efficiency, and security across the ML pipeline while partnering with ML teams.

Lead design, development, and production deployment of ML models and GenAI/LLM solutions to optimize clinical trial workflows. Build scalable pipelines, monitoring, and CI/CD for robust production use. Collaborate cross-functionally, mentor junior engineers, and communicate technical results to technical and non-technical stakeholders.

The role involves developing end-to-end machine learning solutions, focusing on NLP and computer vision, collaborating with teams, and preparing technical briefings for stakeholders.

As an Applied ML Software Engineer, you will design and implement ML models, manage cloud infrastructure, and optimize backend systems for AI-driven content discovery and recommendations.

The ML Platform Engineer will build scalable architecture for ML training workloads, optimize system performance, and collaborate with teams for enhanced efficiency on Kubernetes.