Top Machine Learning Engineer Jobs

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.

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 and deploy production-grade machine learning models, manage model lifecycles through MLOps practices, and support ML platforms and operations. The role requires strong Python, PyTorch, TensorFlow, Azure, software engineering, and coding expertise. Preferred experience includes generative AI, large language models, retrieval-augmented generation, vector databases, and semiconductor or manufacturing domains.

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.

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.

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

Design and develop scalable, high-performance data and API infrastructure for real-time processing. Mentor engineers and collaborate with teams to enhance AI model evaluations.

Own production safeguards infrastructure for Claude model launches and safety classifier deployments. Configure and verify safeguards across first-party, AWS Bedrock, and GCP Vertex platforms; lead canary rollouts, post-deployment validation, incident response, and rollback decisions. Build automation, continuous validation, repeatable deployment pipelines, and a provenance registry to reduce operational toil and configuration drift. Participate in on-call rotations and launch readiness processes.

Design, build, deploy, and operate production AI/ML systems for generative AI, search, retrieval, ranking, recommendations, personalization, and intelligent reader experiences. Translate research prototypes into scalable, low-latency services; develop ML platforms, feature pipelines, model serving, inference, observability, and optimization systems. Provide technical leadership through architecture reviews, mentoring, and roadmap development.

Leads architecture, implementation, and scaling of production ML and LLM systems for legal products. Builds RAG pipelines, agentic applications, hybrid search, APIs, microservices, data pipelines, and model-serving infrastructure. Establishes reusable AI frameworks, deployment and observability practices, and CI/CD standards while optimizing latency, reliability, scalability, and cost. Partners with data scientists to productionize models and provides technical leadership in system design and engineering excellence.

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.

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.

The Machine Learning Engineer will develop and enhance ML-powered products focusing on models, metrics, pipelines, and collaboration with product teams.

Design, fine-tune, and deploy LLMs and RAG pipelines in private, secure environments. Build multi-agent systems, private inference infrastructure, evaluation frameworks, and observability tooling. Collaborate with platform teams to ensure secure, scalable production AI for enterprise customers.

Develop, train, evaluate, and deploy spatial generative AI models for home renovation and design. Define inference framing, prepare image-based datasets with labeling partners, iterate with user feedback, and deploy performant models into production architecture.

As a Fraud ML Engineer, you will ensure authentic human data is processed by building detection systems using adversarial ML techniques and managing backend systems.

As an ML Engineer, develop and deploy advanced ML solutions for OCR, document processing, and voice technologies, collaborating with teams to optimize models for performance.

Architect, build, and operate ML inference and deployment infrastructure (model serving, orchestration, CI/CD, feature stores). Improve developer experience, optimize GPU/CPU scheduling and performance, and establish ML deployment best practices across Riot’s production ML ecosystem while shaping long-term platform direction.

Join PatternAI's data team as a Machine Learning Engineer, building ML pipelines and managing MLOps infrastructure for model optimization.

Seeking a Perception ML Engineer to develop and deploy advanced perception models for robotics, focusing on manipulation and navigation in dynamic environments.

You'll lead the development of AI-driven systems for a dating platform, focusing on personalized matching algorithms and voice interactions. Responsibilities include designing ML pipelines, deploying models, and conducting experiments to improve user experience.

As a Machine Learning Engineer at Onyx, you will enhance agent and knowledge retrieval capabilities by implementing LLM-based graphs and advanced NLP algorithms, while collaborating with the team to improve user experience and product direction.

The role involves developing, training, and deploying machine learning models, building data pipelines, and collaborating with teams for integration and performance optimization. It supports the ML lifecycle from data collection to monitoring.

The role involves designing, developing, and deploying machine learning models for healthcare solutions, optimizing data processing pipelines, and collaborating with cross-functional teams to enhance patient care.