Staff Software Engineer (NLP)

Company overview

Consumer Edge is a data innovation and AI company transforming how professionals interpret consumer and business behavior. Our platform combines large-scale transaction data with advanced AI systems to deliver real-time insights to enterprise clients. Our technology team is distributed across Europe and North America, united by a focus on precision, scalability, and innovation.

Role summary

We are seeking an experienced Machine Learning Engineer with a specialization in Natural Language Processing (NLP) to solve one of our most critical and complex data challenges: entity resolution. In this role, you will design, build, and deploy production-scale systems to intelligently link records from disparate, massive datasets.

Your work will be foundational, creating a single, unified view of core entities (such as products, organizations, locations, and customers) that powers our analytics, product features, and business strategy. The ideal candidate is a hands-on problem-solver who thrives on end-to-end ownership—from collaborating on project scope and defining data needs to developing novel modeling strategies and deploying robust, scalable systems onto our cloud infrastructure (GCP & AWS).

Your main responsibilities

• Design & Build: Lead the end-to-end development of machine learning pipelines for large-scale entity resolution, record linkage, and data matching.
• NLP Modeling: Apply and customize advanced NLP techniques (e.g., entity extraction, semantic similarity, text vectorization, fuzzy matching) to compare and match entities from structured and unstructured text.
• System Architecture: Engineer scalable and efficient data processing and model inference systems designed to handle terabyte scale datasets using cloud-native tools.
• Deployment: Deploy, monitor, and maintain ML models and data pipelines in production on GCP (e.g., Vertex AI, BigQuery, Dataflow).
• Project Leadership: Collaborate closely with product managers, data engineers, and business stakeholders to scope new projects, define data requirements, and establish success metrics.
• Communication & Documentation: Create clear, comprehensive design documents and effectively communicate complex technical concepts, trade-offs, and results to both technical and non-technical audiences.

Required Experience

• 3+ years of hands-on experience building and deploying machine learning models in a production environment.
• Proven, demonstrable experience in Natural Language Processing (NLP) with a specific focus on entity resolution, record linkage, or data matching projects.
• Strong proficiency in Python and common ML/data science libraries (e.g., scikit-learn, pandas, spaCy, Hugging Face Transformers).
• Hands-on experience with ML deployment and data processing services on public cloud providers (GCP, AWS, or Azure).
• Solid software engineering fundamentals, including version control (Git), testing, and CI/CD practices.
• Excellent written and verbal communication skills, with a proven ability to document design decisions and present complex information clearly.

Desired experience

• Experience building data-intensive applications and working with very large datasets using distributed computing frameworks (e.g., Apache Beam, Apache Spark, Dask, Ray).
• Experience building NLP applications with an LLM based component
• Familiarity with MLOps principles and tools (e.g., MLflow, Kubeflow, TFX).
• Experience deploying AI/ML systems to production and integrating with data pipelines (e.g., ETL tools, Airflow, Dagster).
• Publications in relevant conferences (e.g., ACL, EMNLP, KDD) or contributions to open-source projects.

Tech stack & team context

Benefits & perks

We are a remote-first company with a distributed environment and flexible working arrangements. We believe that distributed workers should be first-class citizens. We also have an office in New York if offices are your thing.

Salary

The annual base salary for this role is between $225,000 – $275,000 based on experience, with the opportunity for a performance-based bonus, company equity, 401(k) matching, paid parental leave, flexible and generous time off, work-from-home flexibility, and subsidized health benefits.