ML / AI Jobs in United States
There are 4,262 open ML / AI jobs in United States on freehire right now. 799 of them were posted recently. The skills employers ask for most often are machine-learning, ai and python.
Salary
| Currency | Period | 25th | Median | 75th | Postings |
|---|---|---|---|---|---|
| USD | year | $190,600 | $223,000 | $256,500 | 1,033 |
| USD | hour | $30 | $40 | $60 | 36 |
From postings that disclose pay. Currencies are counted separately, never converted.
Most requested skills
- machine-learning 90%
- ai 77%
- python 61%
- pytorch 40%
- cloud 38%
- llm 37%
- deep-learning 27%
- tensorflow 26%
How the work is done
- Remote 693 · 16%
- Hybrid 531 · 12%
- Onsite 383 · 9%
Visa sponsorship offered in 63% of the 1,014 postings that state a position on it.
Seniority
- Senior 1,214
- Staff 466
- Lead 218
- Principal 205
- Intern 104
- C-level 58
Who is hiring
- 1000+ employees 1,182
- 501-1000 employees 278
- 11-50 employees 127
- 51-200 employees 101
- 201-500 employees 26
- 1-10 employees 5
Machine Learning Engineer II, Responsible AI
Machine Learning Engineer II on Pinterest's Responsible AI team, building fairness interventions, bias mitigation, and ethical AI safeguards for large-scale recommender systems and generative AI.
Senior Software Engineer - Machine Learning Platform
Build and operate Upstart's ML and simulation platform infrastructure—model training, feature engineering, inference, and marketplace simulation—using Python, Kotlin, AWS, Spark/Databricks, and MLOps tooling.
Software Engineer III, AI/ML
This role involves developing AI-driven security platforms using RAG, agentic workflows, and Google's threat intelligence. Engineers will write production code, optimize ML models, and collaborate on system design and debugging.
Software Engineer III, AI/ML, gUP Customer Support
Develops AI/ML-powered software systems for Google’s gUP Customer Support, focusing on user voice and AI assistant features. Works on full-stack solutions, model integration, and 24x7 support systems while collaborating with engineering, product, and UX teams.
Cloud AI/ML Architect 1r
Senior AWS Enterprise Architect leading enterprise-scale cloud, data, infrastructure, and AI/ML solution design within a healthcare technology environment, focusing on AWS, data architecture, security, and analytics.
Senior Machine Learning Engineer, Agent Eval Platform
This role involves building an agent evaluation platform to measure and improve AI agent performance through calibrated judges and reward models. You will design rubrics, calibrate against human labels, and develop methodologies to train agents using LLM-based evaluation.
AI/ML Engineer
Machine Learning Engineer at Cinder building classification pipelines, confidence cascading, and model training/serving infrastructure for a trust & safety platform, using Python, PyTorch, scikit-learn, XGBoost, and LLMs.
Senior Machine Learning Engineer
The Senior Machine Learning Engineer will build and evolve cloud-based AI/ML solutions to enhance enterprise service workflows. The role involves collaborating with cross-functional teams to design, implement, and deliver scalable AI-powered software using Java or Python.
AI/ML Engineer
Builds and deploys AI/ML models (generative AI, NLP, computer vision, reinforcement learning) for government clients, focusing on cloud-based solutions and data pipelines.
Software Engineer (AI/ML & Cloud Applications)
Software engineer building GUIs and dashboards, deploying/managing AWS applications, automating workflows, and integrating AI/ML models for a government consulting firm in Fort Meade, MD.
Staff AI/ML Engineer
Design and deploy AI/ML systems for ICS/xOT cybersecurity, building threat detection and automation models in cloud and on-prem environments using Python, PyTorch/TensorFlow, and Kubernetes.
Senior AI/ML Engineer
Design and deploy AI/ML systems for industrial cybersecurity, focusing on threat detection and automation in ICS/xOT environments using Python, ML frameworks, and Kubernetes.
Machine Learning Engineer - Remote
This role involves creating reinforcement learning environments and deterministic verification systems to evaluate AI models on complex software engineering tasks like bug fixing and refactoring. Engineers will use Model Context Protocol (MCP) tools to build reproducible scenarios for AI agent training.