Machine Learning Engineer
Numbers Station - Menlo Park, CA
posted 2 days ago
About the position
At Numbers Station, we're using the latest revolution in artificial intelligence to automate enterprise data tasks. In particular, we use foundation models (e.g. GPT-4o) to power data analytics and downstream actions. Ultimately this democratizes access to data within organizations and enables them to quickly act on those insights. Numbers Station is born out of years of research at Stanford University and our technology already powers assistants and search engines at top tech companies. Numbers Station is bringing this technology to the broader enterprise. Excited to join a young startup redefining how enterprises use AI? Apply to be a Machine Learning Engineer on the Numbers Station team. As a Machine Learning Engineer, you'll engage new ML use cases through hands-on customer problems and generalize learnings into the core Numbers Station platform. With a combination of strong programming skills and a user-focused mindset, you will have full ownership and responsibility for building, shipping, and maintaining innovative new functionality across the stack.
Responsibilities
- Improve accuracy of our production machine learning systems by applying the latest published research and your own creative solutions, including but not limited to prompt tuning, and guarding against hallucinations
- Own end-to-end accuracy and performance for our machine learning solutions
- Stay up to date on the latest research publications, and rapidly assess the latest results for production and scalability potential
- Work closely with product leadership and customers on training datasets and error analysis
Requirements
- 3+ years of professional experience with machine learning, or 1+ years of professional experience with machine learning with an advanced degree in a relevant field
- Expertise in modern machine learning frameworks and technologies (e.g. PyTorch, TensorFlow, transformers), and an obsession with thorough ML evaluation
- Strong coding skills and knowledge of ML techniques and algorithms
- Ability to work in a fast-paced environment and navigate ambiguity
- Strong technical communication skills
- Self-motivation, positive attitude, and a growth mindset
Nice-to-haves
- Experience using and developing with foundation models
- Experience working directly with customers on machine learning problems
- Experience building and maintaining large scale, production data pipelines for machine learning applications
Benefits
- Competitive compensation package
- Startup equity
- Health insurance
- Other benefits
