Machine Learning Engineer (Python) - Remote

Overview

Job Title: Senior Full-stack Engineer
Work Arrangement: Remote| Must be able to work EST hours
Job Type: Full-time
Salary: Competitive base salary in USD
Industry: PropTech / B2B SaaS / Real Estate Technology
Work Schedule: 40 hours per week

About Pearl Talent

Pearl works with the top 1% of candidates from around the world and connects them with the best startups in the US and EU. Our clients have raised over $5B in aggregate and are backed by companies like OpenAI, a16z, and Founders Fund. They’re looking for the sharpest, hungriest candidates who they can consistently promote and work with over many years. Candidates we’ve hired have been flown out to the US and EU to work with their clients, and even promoted to roles that match folks onshore in the US.

Hear why we exist, what we believe in, and who we’re building for: Watch here

🧑‍💻 Why Work with Us?

We’re not just another recruiting firm—we focus on placing candidates with exceptional US and EU founders who prioritize the long-term success of their team members. We also provide retention bonuses at 3, 6, 9, and 12 months, as well as community-driven benefits like an annual retreat.

🏢 About the company

Our partner company helps the world’s most prominent companies navigate their most important brand, reputation, and product challenges. We specialize in high-impact research with hard-to-reach audiences -- recruiting the exact audiences our clients need, anywhere in the world. Our in-house teams ensure rigorous quality, rapid execution, and clear, strategic insights. Every engagement is custom-built, senior-led, and designed to deliver answers that drive key decisions.

Key Responsibilities

·      Train and evaluate ML models using common machine learning frameworks in Python. Examples include TensorFlow, Keras, scikit-learn, or PyTorch.

·      Develop and refine NLP pipelines (e.g., tokenization, entity recognition, similarity models).

·      Perform fine-tuning and prompt engineering for LLMs (GPT, Claude, etc.).

·      Create semantic search and recommendation models using vector embeddings and clustering techniques.

·      Conduct experiments, hyperparameter tuning, and performance benchmarking.

·      Collaborate with software engineers to integrate models into backend systems.

·      Prepare clear documentation, model cards, and evaluation reports.