ML / AI Jobs in United States
There are 4,485 open ML / AI jobs in United States on freehire right now. 1,045 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 | $194,895 | $227,500 | $262,500 | 805 |
| USD | hour | $45 | $70 | $90 | 69 |
From postings that disclose pay. Currencies are counted separately, never converted.
Most requested skills
- machine-learning 95%
- ai 82%
- python 71%
- pytorch 47%
- llm 45%
- cloud 44%
- deep-learning 32%
- tensorflow 31%
How the work is done
- Remote 832 · 19%
- Hybrid 553 · 12%
- Onsite 521 · 12%
Visa sponsorship offered in 56% of the 514 postings that state a position on it.
Seniority
- Senior 1,104
- Staff 441
- Lead 312
- Intern 244
- Principal 202
- C-level 139
Who is hiring
- 1000+ employees 556
- 501-1000 employees 145
- 11-50 employees 67
- 51-200 employees 51
- 201-500 employees 15
- 1-10 employees 4
Perception Machine Learning Engineer – ADAS
Build and optimize perception algorithms for autonomous driving using camera and LiDAR data, focusing on deep learning models for object detection, tracking, and segmentation.
AI/ML Engineer
LCAT Description "Research, design, develop, and test operating systems-level software, compilers, and network distribution software for medical, industrial, military, communications, aerospace, business, scientific,…
AI/ML Engineer
LCAT Description Degree in Computer Science, Information Assurance, Information Security System Engineering, or related discipline from an accredited college or university. Experience in software development on…
Machine Learning Engineer
Kargo creates powerful moments of connection between brands and consumers to build businesses. Every day, our dynamic teams work to radically raise the bar on what agentic AI, CTV, eCommerce, social, and mobile can do…
AI/ML Engineer SME (TS/SCI with Poly Required)
GCI embodies excellence, integrity and professionalism. The employees supporting our customers deliver unique, high-value mission solutions while effectively leverage the technological expertise of our valued workforce…
Staff Software Engineer, Machine Learning
Staff-level, hands-on ML infrastructure engineer at Current, a consumer fintech in NYC, who owns the end-to-end ML stack: feature computation, training data, serving, and monitoring, so data scientists can ship models faster and reproducibly. Core tech: Python, SQL, JVM (Apache Beam/Dataflow), GCP (GKE, BigQuery), Airflow, dbt.
Machine Learning Performance Engineer
Optimizes deep-learning pipelines and GPU frameworks to speed up AI-driven trading strategies, working with PyTorch, JAX, CUDA and low-level GPU programming.
AI/ML & Algorithm Engineer
AI/ML & Algorithm Engineer at Salute, a data center infrastructure services provider. Builds production multi-agent AI systems, LLM/RAG workflows, and novel scoring/optimization algorithms for the company's enterprise platform, plus evaluation, governance, and patentable AI designs, using Python, PyTorch/JAX, LangChain/LangGraph, and vector databases.
Member of Technical Staff, Machine Learning
Build and deploy core ML components for an AI-native assistant that automates daily tasks with high reliability and low latency using Python, PyTorch/JAX, and GPU-based production systems.
Applied Machine Learning Engineer
Builds and owns end-to-end ML systems for a proactive AI assistant, focusing on long-running workflows, reliability, and real-world task completion using PyTorch/JAX and GPU infrastructure.
Machine Learning Engineer, Ranking & Retrieval
Build and improve the ML systems behind an AI-native mobile communication app — working on classification, ranking, retrieval, personalization and evaluation from model development through production deployment. Stack centers on Python, PyTorch/JAX, embeddings and vector search.
Technical Lead, Machine Learning
Hands-on ML technical lead owning the end-to-end machine learning stack — data pipelines, training/fine-tuning, evaluation, inference, and deployment — behind AI-native productivity apps. Remote role based in China; core stack is Python, PyTorch/JAX, and GPU-based training and inference systems.
Principal Machine Learning Engineer
Principal ML Engineer building AI-native productivity apps for tasks and workflows. Responsible for the end-to-end ML lifecycle, including training pipelines, evaluation systems, and high-performance GPU inference using Python, PyTorch, or JAX. Focuses on turning research into reliable, scalable production systems for long-running workflows.
Lead Engineer, Machine Learning
The Lead Engineer, Machine Learning will build and scale production-ready AI systems, managing the full model lifecycle from training to deployment. The role focuses on creating reliable, high-performance inference systems using Python, PyTorch, and JAX to power proactive, AI-native applications.
Staff Machine Learning Engineer
Staff ML Engineer owning the end-to-end ML execution layer at an AI-native productivity startup — building training/fine-tuning pipelines, evaluation systems, and high-performance GPU inference to turn large models into reliable production systems for proactive everyday apps (email, notes, tasks, calendar). Stack: Python, PyTorch/JAX, GPU infra.
Senior Engineer, AI/ML (Denver, CO)
Senior engineer who designs, builds, and operates production LLM-based AI agents and multi-agent workflows, primarily in Python on AWS (Bedrock AgentCore, serverless stack), with prompt engineering, evals/guardrails, and observability. Hybrid in Denver (2 days/week in office) or fully remote within the US.