Key skills
PythonGoAIMachine LearningMLNLPOpenAIClaudeAnthropicscikit-learnPandasWorkday
About this role
Go Offer is an AI-powered job search platform that helps professionals land jobs at top US companies. They are seeking a Staff ML Engineer to own the machine learning layer of their platform, focusing on designing models and shipping code that impacts real users.
Responsibilities:
- Own the machine learning layer of our platform
- Designing models
- Writing code
- Shipping things that real users interact with every day
- Building models that understand what makes a resume a strong fit for a specific role, beyond keyword overlap
- Understanding how applicant tracking systems score resumes and reverse-engineering that into actionable rewrites
- Models that figure out the right message, the right person, and the right timing for cold outreach at scale
- Knowing which 500 jobs out of 10,000 are actually worth applying to for a specific candidate profile
- Pulling patterns from successful and unsuccessful interview outcomes to improve prep recommendations
Requirements:
- You've built ML models that went into production and affected real users — not just notebooks and experiments
- Strong in Python — pandas, scikit-learn, and whatever else gets the job done
- You understand NLP and text modeling well enough to work with resume and job description data
- You can own a problem end-to-end — from defining what to measure, to building the model, to shipping it, to knowing if it worked
- You've worked in ambiguous environments where the problem wasn't handed to you pre-packaged
- Experience with LLMs and prompt engineering — we use Claude (Anthropic) and OpenAI heavily across the platform and expect our ML engineers to know how to work with and around them
- Comfortable working with small teams and without heavy process — we don't have six layers of approval, we have a problem and a deadline
- Experience in HR tech, recruiting, or career services — understanding the job search process from the inside helps
- Experience building ranking or recommendation systems
- Familiarity with ATS systems (Greenhouse, Lever, Workday, iCIMS) and how they parse and score resumes
- Experience working with unstructured text data at scale