Top Machine Learning Engineer Jobs

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.

Build and scale LLM-based, agentic customer-engagement systems: architect, deploy, and monitor production ML features; finetune models; create evaluation frameworks, feedback loops and HITL workflows; run A/B tests; optimize latency, cost, and reliability; and mentor the team.

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.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Own end-to-end ML model lifecycle from EDA and feature engineering to production deployment and monitoring. Collaborate with cross-functional teams to identify modeling opportunities, evaluate performance and business impact, integrate new data sources, and ensure scalable, observable production systems.

Design, develop, and deploy state-of-the-art deep learning models for multimodal and satellite imagery; own ML architecture, infrastructure, model registries, and inference services; mentor the team, translate product vision into technical roadmaps, and integrate research into production-ready, scalable geospatial AI systems.

Design, implement, and scale production-ready ML systems (computer vision and LLMs) for blueprint understanding, 3D model generation, document extraction, and materials forecasting. Build ML pipelines, deploy and monitor models, integrate retrieval/RAG and rules-based methods, and collaborate with product, engineering, and domain experts to deliver explainable, business-impacting features.

Build and scale the infrastructure layer for visual intelligence: optimize VLM inference and GPU serving, design multimodal APIs and structured outputs, ensure scalable backend systems, reliability, observability, CI/CD, and developer experience. Drive 0→1 product work with strong testing discipline, schema validation, and performance optimization.

Lead design, development, and deployment of production-scale ML and AI systems. Own end-to-end ML lifecycle, architect scalable pipelines and infrastructure, mentor engineers, integrate emerging AI (LLMs, RAG, agents), optimize models for edge inference, collaborate with cross-functional teams, and ensure monitoring, reliability, and ethical considerations for law enforcement and homeland security solutions.

Design, build, and operate large-scale offline data infrastructure and pipelines for ML training and experimentation. Improve reproducibility, observability, performance, and cost-efficiency across batch and streaming workloads while integrating orchestration systems and partnering with ML engineers.

Lead design and implementation of post-training alignment pipelines (SFT, RLHF, RLAIF) using proprietary medical data. Improve model calibration, safety, and clinical correctness via novel techniques, distributed training on GPU clusters, and rigorous evaluation frameworks. Collaborate with co-founders to define research roadmap and ship production-ready ML systems.

Work as a hands-on junior ML engineer building generative AI apps, traditional ML models, and MLOps solutions. Implement and maintain model training, inference pipelines, and LLM systems while being mentored by senior engineers and delivering client projects.

Build and productionize scalable ML infrastructure for training, evaluating, and serving large language models. Develop distributed training and inference pipelines, optimize LLM latency, create automation abstractions, and collaborate cross-functionally to scale ML systems and best practices.

Design, build, and optimize scalable ML infrastructure for training, evaluating, and serving large language models. Automate ML workflows, improve LLM latency and production pipelines, and collaborate cross-functionally to deploy and monitor models at scale.

Design, build, and productionize real-time fraud detection models. Advance adversarial-domain techniques (graph, sequence, anomaly detection), design robust evaluation for biased/delayed labels, ensure low-latency reliable deployment, monitor models in production, and collaborate with data scientists and stakeholders to translate fraud signals into modeling solutions.