ML Engineer

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 an auto-apply platform that helps job seekers find fresh job postings, tailor their resumes and cover letters, and submit applications quickly across systems like Greenhouse, Lever, Ashby, and Workable. We're looking for an ML Engineer to join our Ranking & Relevance team, responsible for the models and systems that match job seekers to the roles most relevant to them and score fit across millions of live postings.

What you'll do

• 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

What we're looking for

• Experience building and shipping 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
• Experience with large-scale data processing and feature pipelines
• Comfort working with experimentation frameworks and interpreting A/B test results
• Strong communication skills and ability to work cross-functionally with product and engineering

Nice to have

• Experience with embedding-based retrieval or vector search systems
• Background applying ranking/relevance models specifically to matching problems (jobs, dating, e-commerce, content)
• Familiarity with real-time or near-real-time ML serving infrastructure