What are the responsibilities and job description for the Machine Learning Engineer position at Root Access?
Company Overview:
Root Access is an applied AI company building developer tools. We help mission-critical hardware teams leverage purpose-built AI to program and certify their systems faster.
Role Description:
The Machine Learning Engineer will be responsible for designing and developing machine learning systems, implementing appropriate ML algorithms, conducting experiments, and improving the product. They work with data to create models, perform statistical analysis, and train and retrain systems to optimize performance. Their goal is to build efficient self-learning applications that will delight customers. This is an early-stage company with ambitious goals.
You might be a good fit if you have:
Root Access is an applied AI company building developer tools. We help mission-critical hardware teams leverage purpose-built AI to program and certify their systems faster.
Role Description:
The Machine Learning Engineer will be responsible for designing and developing machine learning systems, implementing appropriate ML algorithms, conducting experiments, and improving the product. They work with data to create models, perform statistical analysis, and train and retrain systems to optimize performance. Their goal is to build efficient self-learning applications that will delight customers. This is an early-stage company with ambitious goals.
You might be a good fit if you have:
- Proficiency in PyTorch and modern-transformer based systems
- Experience with AWS for scalable ML service deployment
- Experience building with Agentic AI frameworks (e.g., RAG, Langchain, MCP, etc)
- Have 1-3 years of full-time experience in an MLE role
- Strong ML Foundations - Experience with recommender systems, embeddings, foundation models. You understand when to use the fancy stuff—and when to keep it simple.
- Production Mindset - You’ve shipped ML systems that run in the real world. You write reliable Python, know your way around infra basics, and care about performance.
- Data Agility - You’ve worked with messy data—scraping, parsing, cleaning, and transforming it into something your models can learn from.
- Frontend Awareness - You’re not expected to be a frontend engineer, but you know how to make ML feel native in a modern React-based product.
- High Ownership DNA - You see the problem, spec the solution, and ship. You don’t need permission—you need a challenge.
- 1-of-1 Energy - You’ve been underestimated, or boxed in. You're ready to work somewhere that lets you fully show what you're capable of.