> Markdown version of [/jobs/ext/2101358-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/2101358-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Viasat, Inc. - **Location:** San Jose, CA, United States - **Experience:** Expert - **Salary:** $140,500.0 - $221,500.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Airflow, Amazon Web Services, BigQuery, Cloud Engineering, Data Presentation, Linear Programming, Machine Learning, Tensorflow, Standard Sql, Reinforcement Learning, Google Cloud, Cloud Platform System, Pytorch, Orbital Mechanics, Optimization Algorithms, Data Analytics, Machine Learning Operations, Restful APIs - **Published:** August 18, 2026 - **Apply:** https://www.dice.com/job-detail/1fe51366-1ac9-4915-9c25-acd4d37ea96b ## About the Role 7+ years in ML/optimization roles - Strong background in optimization techniques (linear programming, constraint satisfaction, etc.) - Experience building production ML systems in cloud environments (AWS/Google Cloud Platform) - Experience with ML frameworks (TensorFlow, PyTorch, or equivalent) - Skilled at building geospatial visualizations and telling data-driven stories - Cloud-native containerized application development experience - Proficient in SQL - Comfortable building and maintaining RESTful APIs - Ability to travel up to 10% What will help you on the job - Experience in satellite communications domain - link budgets, orbital mechanics - Production reinforcement learning systems experience - Airflow, Athena, BigQuery - Experience with ECS, Batch, or similar orchestration tools ## Description You will be a hands-on ML engineer building platforms and tooling to optimize supply allocation across our global satellite fleet to meet customer demand, and provide business stakeholders with planning insights. The day-to-day - Develop models, optimizations, and tools for dynamic supply-demand optimization scenarios - Contribute to geospatial visualizations and data-driven storytelling for planning insights - Build and maintain production ML systems in cloud-native, containerized environments - Collaborate with SMEs and stakeholders to understand requirements and validate optimization approaches - Implement and iterate on demand/supply demand/supply modeling strategy and validate feasibility of optimization approaches - Build and maintain APIs that expose ML capabilities to downstream consumers ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)