Machine Learning Engineer

WireScreen offers a hybrid work setup in New York, NY for a Machine Learning Engineer. You will join a team focused on scalable data infrastructure, knowledge graph development, and deployment of ML models across millions of records. This role reports to the VP of Engineering and partners with data, enrichment, and product teams. The position comes with a competitive total compensation package, including a salary in the range of $150,000 to $195,000 per year, potential equity, and strong opportunities for growth.

Responsibilities

• Fine tune our existing entity resolution algorithms to reveal hidden connections between people and organizations across China.
• Enrich the knowledge graph by incorporating alternative data sources to map the power structure of China.
• Develop, validate, and deploy ML models that operate on tens of millions of records daily.
• Collaborate with Product to define and implement evaluation harnesses for classical ML and agentic systems.
• Integrate agent workflows into internal tools to scale and accelerate the Research team's work.

Requirements

• 4+ years of experience tackling clustering type ML problems, ideally in knowledge graphs or entity resolution, with related domains such as recommendation engines, cohort analysis, or anomaly detection also relevant.
• End-to-end production ML experience, including standing up a service from experimentation and training to testing, tuning, deployment, and ongoing maintenance. Model families may include clustering, classification/regression, dimensionality reduction and embeddings, nearest-neighbor methods, ensembles, NLP, and deep learning.
• Strong experience with Python and SQL.

Technologies

• Python
• SQL
• PySpark
• Temporal
• FastAPI
• Scikit-learn
• NumPy
• Docker
• Terraform
• Kubernetes

Benefits

• Competitive compensation including salary, equity, and rapid growth potential
• 100% company-paid Medical, Dental, and Vision coverage for employees
• FSA, HSA, and 401(k) options to help you plan for healthcare expenses and retirement
• Generous paid time off plus company-wide holidays to help you rest and recharge
• Pre-tax commuter benefits to help you save on transit and parking
• Hybrid office schedule designed to give you flexibility while staying connected with your team

Nice to Have

• Experience working with Frontier or state-of-the-art models and/or fine-tuning LLMs for specific tasks
• Experience with large, heterogeneous unstructured datasets and or semantic search, computer vision (OCR), or linear optimization problems
• Familiarity with PySpark, Temporal, FastAPI, Scikit-learn, NumPy, Docker, Terraform, Kubernetes
• Early-stage startup experience (Series B or earlier)
• B2B SaaS experience