The role, as published
About the company
Our client is a fast-growing technology company.
The role
Raydar is recruiting for this role on behalf of our client. Apply machine learning in a live production environment, improving models that handle sensitive text and other data types. The work involves studying model weaknesses, preparing training data, running experiments and releasing measurable gains. It is a high-ownership position within a small, in-person engineering team.
What you'll do
- Own a production model end to end, including retraining, failure analysis and release of measurable accuracy improvements.
- Design and scale data labeling approaches, including active learning and LLM-assisted review.
- Extend existing capabilities to new data types such as audio, video and tabular data.
- Combine rules-based methods, classical ML, fine-tuning and LLM techniques to solve entity recognition and resolution problems, choosing the approach based on evidence.
- Build evaluation benchmarks and experiment tooling so that improvements move from discovery to production faster and more repeatably.
What we're looking for
- 3+ years of experience releasing and improving machine learning models in production.
- Strong Python software engineering skills, with experience in production-grade code rather than only exploratory notebooks.
- Experience working with AWS.
- Track record of investigating why a model underperforms on unfamiliar inputs, curating training data and lifting both precision and recall.
- Background in natural language processing, ideally with text extraction or entity-focused tasks.
- Experience expanding labeling throughput or adapting models to additional data types, plus familiarity with fine-tuning transformer models and inference optimization.
- Startup experience and a clear record of career progression.
Bonus points
- STEM undergraduate degree from a highly ranked US university.
Benefits
- Health, dental and vision coverage
- Paid time off and company holidays
- Wellness benefits
Location and work model
- New York, NY, United States
- On-site, 5 days per week in office
- Full-time
Compensation, as the posting describes it
- Equity