About the Role
Ping Data Intelligence is a dynamic startup based in Miami, FL, revolutionizing the property insurance sector with cutting-edge web technologies and ML-powered tools. Despite rapid growth, we retain the stability of a self-funded, profitable company.
Role Overview: As a Senior ML Engineer at Ping, you will sit at the intersection of research and data engineering — designing, training, and deploying machine learning models that power our property attribute classification, document extraction, and geospatial products. This is a hands-on role for someone who can read a paper in the morning, prototype an idea by lunch, and ship it to production by end of week. You will own ML systems end-to-end: from data pipeline design and feature engineering through model training, evaluation, and production deployment. The role is remote-friendly, with the option to work onsite at our Miami, FL office.
Responsibilities:
Design, train, fine-tune, and evaluate ML models (LLMs, classification, sequence models) for property insurance. Build and maintain robust data pipelines that feed training, evaluation, and inference workloads at scale. Develop rigorous evaluation frameworks — establish metrics, build rater alignment processes, and apply statistical methods to determine when a candidate model is genuinely better than production. Run controlled experiments, ablations, and A/B tests; communicate findings clearly with appropriate uncertainty quantification. Deploy models to production and own their performance, drift monitoring, and iteration cycles. Collaborate with the engineering team to integrate ML services into our backend (Django/Python) and frontend (React/TypeScript) products. Stay current with the ML literature and translate relevant advances into practical improvements for our products.
Required:
PhD in Statistics, Machine Learning, Computer Science, Applied Mathematics, or a closely related quantitative field (or equivalent research experience with a strong publication or production track record). Strong foundation in statistics — experimental design, hypothesis testing, Bayesian methods, and uncertainty quantification. Minimum 5 years of combined research and applied ML experience, with a proven track record of shipping models to production. Deep proficiency in Python and the modern ML stack (PyTorch, Hugging Face, scikit-learn, pandas, NumPy). Hands-on experience with LLMs, including fine-tuning (LoRA/QLoRA, full fine-tunes), prompt engineering, and evaluation. Strong data engineering skills — comfort building reliable pipelines over messy real-world data, working with SQL and columnar formats. Excellent debugging, problem-solving, and written communication skills.
Why Join Ping: Work directly on ML systems that touch real production traffic from day one. Collaborate with a small, senior team of insurance and tech veterans building products that are reshaping the property insurance industry. Enjoy the autonomy of a research role with the impact of an applied one — your models will be in production, used by real customers, and you will see the results immediately.
Please apply at jobs@pingintel.com
