ML / AI Jobs in United States
There are 4,264 open ML / AI jobs in United States on freehire right now. 788 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 | $190,300 | $223,000 | $256,500 | 1,035 |
| 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 38%
- deep-learning 27%
- tensorflow 27%
How the work is done
- Remote 697 · 16%
- Hybrid 530 · 12%
- Onsite 382 · 9%
Visa sponsorship offered in 63% of the 1,015 postings that state a position on it.
Seniority
- Senior 1,218
- Staff 466
- Lead 215
- Principal 205
- Intern 105
- C-level 59
Who is hiring
- 1000+ employees 1,181
- 501-1000 employees 278
- 11-50 employees 127
- 51-200 employees 101
- 201-500 employees 26
- 1-10 employees 5
Artificial Intelligence & Machine Learning Architect SME
Architect scalable AI/ML solutions for an enterprise modernization initiative, focusing on cloud infrastructure, MLOps, and the full ML lifecycle.
Machine Learning Engineer - Ads
Build and deploy machine learning models to power Nextdoor's ad platform, focusing on real-time decisions, personalization, and business impact while collaborating with product and data science teams.
Staff Machine Learning Engineer - Ads
Build and optimize large-scale ML systems for Coupang’s ad platform, focusing on ad targeting, relevance, and ranking using transformers, embeddings, and vector search.
Staff Machine Learning Engineer - Ads
Build and optimize large-scale machine learning systems for Coupang’s ads platform, focusing on ad targeting, relevance, and ranking using transformer models, embeddings, and vector search.
Senior Engineer, Physical Design AI/ML
Develop AI/ML models to optimize chip design PPA and automate PDK/EDA workflows for sub-2nm nodes. Requires expertise in Python, PyTorch, physical design, and generative AI frameworks within a semiconductor R&D lab.
Senior Machine Learning Engineer
Builds AI-driven robotics systems for physical-world automation (food, mining, transport) by designing and deploying ML models (LLMs, GNNs, deep learning) for real-world control, prediction, and optimization in production environments.
Senior Machine Learning Engineer
Build and deploy AI models (LLMs, GNNs, deep learning) for robotics and logistics in food and other industries, turning physical operations into scalable, automated systems.
Machine Learning Scientist, BioML
Design and train deep generative models to design novel proteins using AI, collaborating with biology and ML teams to validate and deploy solutions.
Machine Learning Engineer - ML Training Platform
Pluralis Research works on Protocol Learning: training and serving large models in a fully decentralized way on small consumer-grade devices connected via the internet. Despite being dismissed as infeasible, we have…
Senior Machine Learning (ML) Engineer
Senior Machine Learning Engineer Truveta is the world’s first health provider led data platform with a vision of Saving Lives with Data. Our mission is to enable researchers to find cures faster, empower every…
AIML - Sr Machine Learning Research Scientist, Data and ML Innovation
Would you like to join a team curious about understanding how foundation models work and to expand their capabilities in scientific domains? We perform and publish novel research and apply our findings to drive product…
Senior Applied AI/ML Scientist
Build and fine-tune large language models for healthcare, creating conversational AI that automates claims and improves patient experiences using Python, PyTorch, and cloud ML platforms.
Applied Machine Learning Scientist (Remote)
Build and deploy ML algorithms to optimize ad performance and ROI, writing production code to enhance StackAdapt’s AI-powered marketing platform.
Software Engineer, Inference AI/ML
Develops and optimizes AI model-serving systems on GPU infrastructure, focusing on latency, reliability, and cost while working with tools like Triton, vLLM, and Kubernetes.