Job Title: Staff Machine Learning Engineer (Ranking & Recommendations)
Location: Fully Remote (US only)
Compensation: $220,000 base + bonus + equity
A fast-growing technology company is embedding machine learning into high-volume, high-impact decision systems across a complex, legacy-heavy industry. Backed by a leading private equity firm and strong internal champions, the team is scaling intelligent, production-grade ML solutions that are already delivering significant ROI.
This role offers the opportunity to lead technical direction, mentor peers, and build robust, scalable ML infrastructure in a greenfield environment.
Why This Role Matters
ML is not an experiment here - it’s already powering critical workflows that influence revenue and efficiency. But only a fraction of the potential value has been unlocked. As a senior IC, you’ll shape the next generation of systems and help the company move faster, smarter, and at scale.
This is a high-impact position with influence across the ML stack, strong visibility, and clear opportunities for advancement.
What You’ll Do
- Build and deploy models for ranking, prioritization, and recommendations
- Lead experimentation, modeling strategy, and evaluation practices
- Design and scale robust ML systems from exploration to production
- Collaborate with product, engineering, and operations to align models with business goals
- Mentor team members and help establish modeling best practices
Tech Stack
- Languages/Frameworks: Python, Scikit-Learn, PyTorch, TensorFlow, Hugging Face
- Infra: Spark, SQL, Airflow
- Deployment: AWS, Azure, Docker, CI/CD pipelines
Ideal Candidate
- Expertise in ranking, search, personalization, or recommendation systems
- Track record of shipping production ML systems that drive measurable impact
- Strong communicator and mentor with leadership experience
- Bonus: MLOps, Azure, Kaggle, or agent-based architecture experience
About Us
People In AI partners with high-growth AI and ML companies to bring clarity and precision to technical hiring. We help candidates engage with innovative teams in a transparent and streamlined process.
