Top Machine Learning Engineer Jobs

Design and implement scalable ML solutions for customer experience problems. Analyze customer data, research and deploy ML/NLP models, automate model development and validation, and produce performance analyses.

The Staff AI/ML Engineer will lead the development of AI systems for regulatory compliance, focusing on ML architecture, implementation, and oversight of model reliability.

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

Design and implement reinforcement learning algorithms for robotics, conduct experiments, optimize models, and collaborate with cross-functional teams.

Design, build, and deploy machine learning solutions. Collaborate with teams to transform research into practical applications using large datasets.

Design and implement scalable backend services and distributed, event-driven data pipelines. Build, deploy, and monitor production ML models (feature engineering through deployment). Integrate ML models with rule-based decision engines and contribute to AI/LLM-driven orchestration. Collaborate across engineering, data, and product teams to deliver reliable, production-grade systems.

The Senior Vision AI Engineer will develop and deploy computer vision and machine learning models, create optimized vision pipelines, and mentor junior engineers, focusing on industrial applications.

Lead inference acceleration and GPU-parallelism optimizations (TP, SP, PP); implement high-performance CUDA/NCCL kernels; deploy videogen and LLM models to production; collaborate with researchers, conduct code reviews, mentor engineers, and optionally improve training efficiency and resource utilization.

Develop, train, and deploy AI surrogate models to enhance engineering simulations for aerospace applications while collaborating with engineers to tackle complex challenges.

Develop and implement AI and ML systems for Defense and Intelligence sectors, focusing on deployment, scalability, and integration of technologies.

Design, develop, and deploy production-grade AI/ML systems (NLP/LLMs, CV, generative models). Build scalable data pipelines, microservices/APIs, and cloud/edge deployments; optimize, version, and evaluate models; integrate solutions into classified operational environments and produce technical documentation.

Design, create, and implement AI and ML systems for Defense and Intelligence sectors, ensuring scalability and integration into various environments.

Lead technical direction and deliver large-scale ML platform infrastructure including feature store, model serving, experimentation, and training pipelines. Drive architecture, mentor engineers, coordinate cross-team initiatives, resolve system-wide issues (latency, reliability, GPU utilization), and define safe ML deployment paths from registration through rollout, monitoring, and rollback.

Lead productionization and optimization of ML models for a global, serverless inference platform. Build benchmarking/evaluation tooling, improve inference performance (quantization, compilation, batching), integrate models with heterogeneous GPUs and accelerators, enhance deployment workflows and observability, and mentor engineers while driving ML production best practices.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

Build and scale production ML systems for model customization and fine-tuning-as-a-service, diagnose model behavior with partners, improve training/evaluation/data pipelines, integrate ML capabilities into AWS-native API environments, and collaborate with Research, Safety, and infrastructure teams to deliver reliable, secure, and scalable model customization workflows.

Responsible for designing and optimizing VLSI circuits, translating requirements into data science problems, and building machine learning solutions. Collaborate on hardware design and integrate models into existing tools.

Join Ello as an AI/ML Engineer to create AI-driven educational products, enhance learning experiences, and analyze educational outcomes through machine learning.

Design, build, and own ML/LLM systems for operations: forecasting, schedule risk detection, document extraction, and agentic systems. Handle end-to-end model lifecycle, deploy and evaluate in production, and integrate predictions into operational tools.

As a Forward Deployed Machine Learning Engineer, you'll build and deploy machine learning models, enhance system performance, and collaborate with Data Science and Engineering teams for effective solutions.

Design, build, and deploy production ML and GenAI solutions from prototyping through serving. Apply supervised/unsupervised/deep learning and NLP to structured and unstructured data, implement data pipelines and feature engineering, evaluate model performance/bias/drift, and collaborate with engineers and product teams to integrate models into services and APIs while contributing to architecture, scalability, and documentation.

Design, build, and deploy K-12 AI features (lesson planning, grading, tutoring, admin analytics) while ensuring FERPA/COPPA compliance and child-safety guardrails. Measure impact, iterate with curriculum and legal partners, and maintain production-grade documentation, testing, and runbooks for deployed AI systems.

Lead design, development, deployment, and monitoring of scalable ML models and pipelines. Integrate models into production software, build automated tests, optimize model performance, and collaborate with engineers, product, and QA. Work with cloud platforms, data processing tools, and next-generation automation to support personalization, ranking, and LLM fine-tuning.

Design and run experiments measuring SL5 controls on real ML workflows; forecast 2028 frontier workloads and infrastructure needs; run and interpret scalable experiments to inform a purpose-built, high-assurance datacenter standard for frontier AI.

Lead development of ML components for medical image reconstruction: define training/evaluation pipelines and metrics, productionize models (inference performance, monitoring, safe fallbacks), collaborate on hybrid physics-ML algorithms, and build tooling for experimentation and rigorous verification.