Machine Learning Engineer

Machine Learning Engineer — PracHub

Location: Remote within the United States
Employment: Full-time
Base salary: USD 120,000–170,000 per year
Visa sponsorship: Available

Apply on the PracHub careers page:
Apply on PracHub

About PracHub

PracHub is a technology interview preparation platform with interview questions, practical guides, and tools for coding, SQL, system design, machine learning, and behavioral interviews. We help people turn interview preparation into focused practice. These roles contribute directly to the product, its content-discovery experience, and the systems that support it.

About the role

Turn machine-learning ideas into reliable PracHub product features. You will build the pipelines, evaluations, and serving components behind relevant search, recommendations, and language-based experiences for interview preparation.

What you will do

• Build reproducible data and model pipelines for search, ranking, recommendations, or language-based product features.
• Create evaluation datasets and offline tests, compare against a baseline, and examine failure cases across relevant content and user scenarios.
• Integrate model or retrieval components into product APIs with clear contracts, sensible error handling, and dependable fallbacks.
• Measure and balance quality, latency, reliability, and operating cost when choosing an approach.
• Monitor behavior after release and investigate retrieval failures, model drift, data changes, and production errors.
• Collaborate with engineering, product, and data teammates; write maintainable code, tests, and documentation that make model changes reviewable.

What we are looking for

• Working proficiency in Python and solid software engineering fundamentals.
• Hands-on experience training, evaluating, or serving machine-learning models.
• Familiarity with APIs, Git, testing, and reproducible data processing.
• Ability to explain evaluation choices and diagnose both model-quality issues and system failures.
• Clear communication and careful handling of user data. Relevant experience may come from employment, research, open-source contributions, or substantial independent projects.

Helpful experience

Experience with information retrieval, embeddings, ranking, recommendation systems, language models, model serving, or observability. Evidence of a well-evaluated, dependable system matters more than using a particular framework.

What good work looks like

Useful outcomes include a measurable relevance improvement, a reliable serving path, or an evaluation that catches a regression before release. We value simple baselines, clear tradeoffs, and systems the team can maintain.

Get to know PracHub

You are welcome to explore our public product information and interview questions to understand the experience you would help improve. Practicing with PracHub or purchasing Premium is optional and does not affect your application.

How to apply

Submit your resume using the application form on our official careers page:
Apply on PracHub

You may include an existing ML project and briefly explain your contribution, evaluation method, serving approach, and a failure case you investigated. If you have already applied for this same role, there is no need to submit a duplicate application.