Remote AI Engineer Jobs
Found 1391 verified opportunities
Remote AI Engineer roles sit at the intersection of software development and data science, letting specialists design, train, and deploy intelligent systems from anywhere with an internet connection. Employers look for hands‑on experience with Python‑based libraries such as TensorFlow or PyTorch, comfort building scalable pipelines on cloud services like AWS or GCP, and the ability to turn research papers into production‑ready models using tools like Docker and CI/CD workflows.
Because the field moves quickly, candidates who can demonstrate real‑world projects—whether a recommendation engine, a computer‑vision API, or an LLM‑powered chatbot—stand out more than those with only academic credentials. Compensation reflects this demand, generally ranging from mid‑six‑figure amounts in North America to competitive mid‑to‑high five‑figure packages in emerging markets, adjusted for seniority and cost‑of‑living.
Remote work fits naturally: model training can be orchestrated on cloud GPUs, code reviews happen via pull requests, and teams collaborate across time zones, giving engineers flexibility while still delivering measurable impact.
AI Clinical EHR Vignette Evaluator (Physician) – Project-Based Opportunities
mindrift
AI Clinical EHR Vignette Evaluator (Physician) – Project-Based Opportunities
mindrift
Senior AI Engineer (RAG Systems & Agentic Workflows)
Provectus
Staff Product Designer (Conversational Agent) at Spotify
Spotify
AI Enablement Manager
Epoch AI
Enterprise Solutions Engineer
Cresta
Lead AI/ML Engineer
Capitaltg
Director of Engineering - AI Patient Engagement Agent
Care Access
Industrial AI Solution Engineer
Cognite
Technical Lead Learning Experience Designer
General Assembly
Technical Lead Learning Experience Designer
General Assembly
Senior Product Designer (AI-First Flows) at ShareGate
Workleap
Senior Product Designer (AI-First Flows) at ShareGate
ShareGate
Analytics Engineer Sênior
Blip
Data Labeling Specialist (Computer Vision) - Construction Drawings
Bobyard
Principal Engineer (AI Systems)
Foundation Health
AI Developer / Software Engineer
omilia
AI Engineer / Data Scientist
Plainconcepts
AI Engineering Industry Mentor
Peopleworth
Senior Enterprise Support Engineer
Unframe
Solution Architect (AI & Hospitality Tech) at Apaleo
apaleo
Software Engineer (LLM Prompting & Orchestration) at XBOW
XBOW
AI Training Project: Psychology Professional (PhD-Level)
Gramian Consulting
AI Training and Evaluation Expert (Insurance Underwriting & Claims)
weekday
Frequently Asked Questions
What salary range can I expect as a remote AI Engineer?+
Pay varies by experience and region, but most remote AI Engineers see compensation that typically ranges from mid‑six‑figure levels in North America to competitive mid‑to‑high five‑figure packages in emerging markets. Senior specialists with a strong portfolio can command the higher end of that spectrum.
Which skills and qualifications are most important for remote AI Engineer roles?+
A solid command of Python and deep‑learning frameworks like TensorFlow or PyTorch is essential, along with experience building end‑to‑end pipelines on cloud platforms such as AWS, GCP, or Azure. Employers also value knowledge of containerization (Docker), CI/CD, and the ability to translate research concepts into production‑grade code.
How can I break into a remote AI Engineer position if I’m just starting out?+
Start by creating a portfolio of real‑world projects—open‑source contributions, a personal model‑deployment repo, or a small AI‑powered web app—to showcase practical ability. Complement that with online certifications in machine learning or cloud AI services, and actively network on remote‑work communities and tech forums.
How do remote AI Engineers ensure their models stay performant after deployment?+
They set up continuous monitoring pipelines that track key metrics like latency, accuracy drift, and resource usage, often using tools such as Prometheus or cloud‑native monitoring services. When performance degrades, engineers retrain or fine‑tune models and redeploy updates through automated CI/CD workflows to keep the system reliable.