ML Engineer - Ranking & Relevance for Job Matching

LandEarly; United States; Remote; USD 120,000 - 190,000; Full time

Job summary

LandEarly is seeking an ML Engineer to join the Ranking & Relevance team. You will design, build, and iterate on ranking and relevance models that match job seekers to roles and score fit across millions of live postings.

You will own the full ML lifecycle from feature engineering to production deployment, collaborate with product and engineering, and build scalable data pipelines for training and evaluation.

Qualifications

• Experience with ranking, recommendation, search relevance, or personalization systems in production.
• Strong programming skills in Python (or similar) and familiarity with ML frameworks such as PyTorch or TensorFlow.
• Solid understanding of information retrieval, learning-to-rank techniques, or recommender systems.

Responsibilities

• Design, build, and iterate on ranking and relevance models that power job-to-candidate matching and fit scoring
• Own the full ML lifecycle: feature engineering, model training, offline evaluation, online experimentation, and production deployment
• Collaborate with product and engineering to define ranking objectives and translate them into measurable model improvements
• Build and maintain data pipelines for training and evaluation at scale
• Run A/B tests and analyze results to validate model changes before full rollout
• Monitor production model performance and diagnose issues related to drift, data quality, or latency

Skills

Information retrieval

Learning-to-rank

Large-scale data processing

Experimentation frameworks

Cross-functional communication

Tools

Python

PyTorch

TensorFlow

Job description

LandEarly is seeking an ML Engineer to join the Ranking & Relevance team. You will design, build, and iterate on ranking and relevance models that match job seekers to roles and score fit across millions of live postings.

You will own the full ML lifecycle from feature engineering to production deployment, collaborate with product and engineering, and build scalable data pipelines for training and evaluation.