Remote TensorFlow Jobs
Found 65 verified opportunities
Remote TensorFlow positions sit at the intersection of deep‑learning theory and production‑grade engineering. Companies looking to scale computer‑vision models, recommendation engines, or speech‑recognition pipelines rely on engineers who can translate research notebooks into reliable services that run on GPUs in the cloud.
In practice, a TensorFlow specialist writes custom layers in Python, leverages the high‑level Keras API for rapid prototyping, and integrates the resulting models with tools such as Docker, TensorFlow Serving, or Google Cloud AI Platform. Employers value hands‑on experience with data preprocessing, model optimization, and monitoring more than a list of academic papers, so a portfolio of end‑to‑end projects—complete with code repositories and performance metrics—often outweighs a pure theory background.
Salary expectations are competitive, typically ranging from mid‑level to senior brackets, with compensation reflecting years of experience, the cost‑of‑living in a remote worker’s region, and the complexity of the deployed models. The remote format fits naturally because training runs on cloud GPUs, collaboration happens over version‑controlled notebooks, and teams can iterate across time zones without the need for a physical lab.
Lead AI/ML Engineer
Capitaltg
AI Engineer / Data Scientist
Plainconcepts
Graphics AI Software Engineer
Plain Concepts
Research Scientist in Interactive Avatars (AI Video Agents)
Synthesia
AI Engineer / Data Scientist
Plain Concepts
Commercial Data Scientist
Doximity
Engineering Manager (AI/ML) at DEUNA
DEUNA
AI Engineer
Creativechaos
Director, Machine Learning
Envoy Global
Staff-Level Machine Learning Engineer (Ads Ranking Org)
Principal Data Scientist
Vidmob
Staff Machine Learning Engineer (Consumer Revenue ML Team) at Discord
Discord
Research Scientist - Magnets Team, SandboxAQ
SandboxAQ
Manager, Machine Learning Engineering
Tala
Machine Learning Engineer (Computer Vision & Visual Language Models)
Orcrist Technologies GmbH
MLOps Engineer - AI Model Infrastructure
weekday
AI Voice Systems Engineer for Elder Care Coordination
Symphony-solutions
Computer Vision Engineer (AI/ML) - Bamboo Team
Terabase Energy
Computer Vision Engineer, Bamboo
Terabase Energy
Data Scientist
Chattermill
Senior Machine Learning Scientist
Chattermill
Staff Machine Learning Engineer
Phantom
Research Data Scientist – GenAI/LLM
Innodata
Senior Learning Experience Designer (LED) - AI & Emerging Technology
General Assembly
Frequently Asked Questions
What salary range can I expect for remote TensorFlow roles?+
Compensation for remote TensorFlow engineers is generally competitive and varies by seniority and region. Mid‑level practitioners often earn a base that falls somewhere between $70k and $110k USD per year, while senior specialists with a track record of production‑scale deployments can see offers from $120k up to $180k or more. Benefits, equity, and cost‑of‑living adjustments also play a big part in the final package.
What are the top skills and qualifications needed for a remote TensorFlow job?+
Employers look for solid Python programming, deep familiarity with the TensorFlow and Keras APIs, and experience deploying models with TensorFlow Serving or cloud services such as Google Cloud AI Platform. A good grasp of data engineering—cleaning, augmenting, and batching large datasets—is essential, as is knowledge of GPU optimization, containerisation (Docker) and version control (Git).
How can I break into a remote TensorFlow position?+
To break into remote TensorFlow work, build a public portfolio that showcases end‑to‑end projects, from data preprocessing to model serving, and host the code on GitHub or GitLab. Complement the portfolio with a recognized certification—such as the TensorFlow Developer Certificate—or a short specialised course that demonstrates practical competence. Participating in open‑source contributions or Kaggle competitions also signals readiness to collaborate in a distributed team.
How does TensorFlow’s graph execution differ from eager execution, and why does that matter for production deployments?+
TensorFlow offers both eager execution, which runs operations immediately and is great for debugging, and graph execution, where the model is compiled into a static computation graph before running. Graph mode enables optimisations like operation fusion, memory reuse, and efficient distribution across multiple GPUs or TPUs, making it the preferred choice for large‑scale production services. Understanding when to switch between the two helps engineers balance development speed with runtime performance.