Role overview
As a Lead Software Engineer in our Machine Learning department, you will take ownership of high-impact initiatives that drive product quality, monetization, and user experience. You'll apply your expertise in Python, data analysis, and modern AI tools to build, deploy, and optimize ML solutions end-to-end. This is a hands-on technical role that blends data science and engineering to deliver measurable business outcomes. You'll collaborate closely with cross-functional stakeholders and contribute to evolving areas such as LLMs, Generative AI, and ML infrastructure. The role offers exposure to complex data challenges and the opportunity to shape Pearl's AI-driven capabilities.
What you'll work on
- Build, train, and deploy ML models that improve product performance and monetization.
- Collaborate with data, product, and engineering teams across multiple internal initiatives.
- Design and implement data pipelines for extraction, transformation, and analysis.
- Apply statistical and machine learning techniques to real-world business problems.
- Work on projects involving LLMs and Generative AI technologies.
- Analyze large datasets to identify trends, insights, and optimization opportunities.
- Integrate ML models into production systems and monitor their performance.
- Communicate findings and recommendations clearly to technical and non-technical stakeholders.
- Experiment with emerging AI tools and methodologies to enhance existing workflows.
- Ensure data quality, reliability, and scalability in all deliverables.
What we're looking for
- 3+ years of hands-on experience in ML Engineering or Data Science.
- 5-6+ years total experience in software or data engineering.
- Strong programming skills in Python.
- Proficiency in SQL for data querying and analysis.
- Solid understanding of data processing, analysis, and visualization.
- Experience with LLMs / Generative AI tools (e.g., OpenAI, Copilot, Cursor).
- Ability to build and deploy ML models end-to-end.
- Strong analytical and problem-solving mindset.
- Experience with .NET for integration work
- Upper-intermediate English proficiency (B2 or higher).
- Familiarity with Databricks or similar data platforms (nice-to-have).
- Exposure to AWS or other cloud-based ML environments (nice-to-have).
Tags & focus areas
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