Top Machine Learning Engineer Jobs

Design and productionize agentic multimodal AI applications and multi-agent systems; architect orchestration, routing, state/memory, and human-in-the-loop workflows; build evaluation and benchmarking frameworks; optimize inference on accelerated hardware (quantization, batching, runtime tuning); profile and solve AI acceleration and memory bottlenecks; collaborate with researchers and developers to deploy and operate large-scale LLM/vision systems.

Act as 'Customer Zero' for the internal ML research platform: benchmark and stress-test distributed training and inference stacks, enable rapid prototyping and agentic/auto-research workflows, identify system bottlenecks across hardware and software, document empirical findings, and advise engineering and research teams on platform improvements.

Lead deployment and runtime operations for ML safety systems: configure and verify safeguards across platforms, run canary rollouts and validations, automate launch runbooks into pipelines, maintain a provenance-backed safeguards registry, and participate in on-call and incident response for safety-critical model releases.

Build and maintain backend and full-stack marketplace features while supporting ML Ops infrastructure (model deployment, feature stores, real-time pipelines, monitoring). Collaborate with data scientists to bring models to production, write well-tested code, address performance and scalability issues, and contribute to technical planning and cross-functional communication.

Build and maintain full-stack marketplace features and support ML Ops infrastructure (model deployment, feature stores, real-time pipelines, monitoring) on Chalk and Vertex AI. Collaborate with data scientists to productionize models, write well-tested code, address performance/scalability issues, and contribute to technical planning.

Apply machine learning to real-time graphics and interactive experiences: develop models from latest research, prototype applications, collaborate across teams, and productize neural graphics features for gaming and visualization.

Build and scale end-to-end ML platforms and generative modeling tools for molecular design. Own data ingestion, training, evaluation, serving, and reproducibility; optimize accelerator utilization; automate ML-to-wet-lab workflows; prototype and deploy models to accelerate drug discovery.

Customer-facing ML engineer who develops, validates, and deploys 2D/3D computer-vision models on CT data for defect detection, dimensional analysis, anomaly detection, and process control. Responsibilities include scoping projects with customers, end-to-end model lifecycle (training, deployment, monitoring, retraining), collaborating with product and engineering to productize solutions, and producing technical documentation. ~30% travel to customer sites.

Design, build, and deploy scalable distributed deep learning pipelines and infrastructure. Optimize hardware utilization, profile and benchmark training, manage large datasets, and enable reproducible ML workflows in cloud environments. Collaborate with ML scientists and engineers to align pipelines with research goals and drive performance, reliability, and operational best practices for large-scale model development.

Design, build, and operate ML and data infrastructure for R&D Quality: productionize models, develop reliable data pipelines and orchestration, implement CI/CD and monitoring, support AI/agent workflows, ensure security and GxP compliance, and partner with data scientists and Quality stakeholders to deliver scalable analytics.

Design, build, and deploy production-ready machine learning models and inference pipelines for enterprise AI use cases. Collaborate with data science, MLOps, and engineering teams to prepare data, implement model training and inference, optimize performance, ensure privacy and responsible AI, write automated tests and documentation, and support model monitoring, troubleshooting, and production integration.

Design, develop, and maintain AI/ML software solutions; collaborate with engineers and leadership; build tools to analyze large-scale data; debug, optimize, test, and document software; leverage modern LLM tools to accelerate development and improve code quality.

Design, build, and operate petabyte-scale data systems for ML training and evaluation. Responsible for ingestion, transformation, lineage, dataset versioning, high-throughput delivery to training jobs, and ensuring data quality and reproducibility. Collaborate cross-functionally and apply strong software engineering practices.

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

Design and implement LLM-powered and agentic AI workflows integrated with mission platforms. Capture user workflows, translate decision points into executable logic, streamline analytics, ensure secure production operation, and collaborate with operators, analysts, and leadership in classified environments.

Design, build, and operate large-scale on-prem Kubernetes platforms (OpenShift/Anthos) for AI/ML and GPU workloads. Own control plane and etcd lifecycle, build controllers/operators in Go, automate via IaC, improve observability and resource utilization, enable ML training/inference/LLM deployments, participate in on-call incident response, and mentor engineers.

Build and optimize high-performance systems for large-scale ML training: profile and remove bottlenecks, optimize distributed training, develop GPU kernels (Triton/CUDA), and engineer high-throughput data pipelines to reduce time-to-convergence and improve reliability and cost-efficiency.

Design, implement, and scale production-ready ML and generative AI systems (including agentic systems, intelligent document processing, and RAG). Collaborate with product, engineering, and data teams to deploy models, establish MLOps best practices, build evaluation harnesses, and document system designs to drive operational efficiency and business impact.

Design, implement, and scale production ML and generative AI systems (including AI agents and intelligent document processing). Collaborate with product, engineering, and data teams, write maintainable Python code, apply MLOps best practices, build evaluation harnesses, and document systems to drive operational efficiency and business impact.

The role involves architecting AI solutions, optimizing ML systems, and deploying advanced retrieval frameworks. Engineers will lead ML lifecycle from research to production, collaborating across teams to build AI-first products.

As a Senior ML/AI Engineer, you'll design and deploy intelligent systems for forecasting, sentiment analysis, and autonomous decision-making. Responsibilities include building ML models, developing agentic architectures, and maintaining production systems at scale.

As a Machine Learning Engineer, you will develop models for NLP and code-generation systems, analyze data, and improve AI performance using advanced algorithms. You'll manage the entire ML lifecycle and ensure effective communication of model benefits across the organization.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.

Own end-to-end ML lifecycle for pricing and underwriting: improve model accuracy and coverage, reduce infrastructure cost, deploy and monitor production models on AWS, run experiments/backtests, and collaborate with domain experts to inform model improvements and product APIs.

The Senior Machine Learning Engineer will develop, deploy, and optimize large language models on healthcare data, collaborate with teams, and ensure compliance.