Machine Learning Engineer, Implicit Signals
$381k - $510k • Remote • San Francisco • FullTime
Posted 3h ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
Machine Learning Engineer
About the job
We are seeking a Machine Learning Engineer to join our team, focusing on developing and deploying classification and evaluation systems. These systems provide crucial feedback to post-training and research teams, enabling them to assess model behavior and drive improvements. This role involves a blend of machine learning expertise, production engineering, and a deep understanding of model lifecycles, from initial experimentation through to deployment and ongoing optimization. You will translate complex questions about model performance into well-defined ML problems, build robust solutions, and ensure their reliability and efficiency at scale.
Responsibilities
- Build, deploy, and operate production classification and evaluation systems.
- Optimize production inference for throughput, latency, and cost.
- Train and optimize classifiers using supervised fine-tuning and distillation.
- Own the reliability and observability of deployed classifiers.
- Design rigorous evaluations to assess model accuracy and identify biases.
- Partner with software engineers to integrate models into reliable execution systems.
- Build reusable engineering tools for classifier development and iteration.
- Translate researcher needs into modeling objectives and production requirements.
Requirements
- Experience taking ML projects from problem formulation through production deployment.
- Strong foundations in supervised learning, statistics, and experimental design.
- Practical experience evaluating models and working with imperfect labels.
- Strong software engineering skills for building maintainable production ML systems.
- Experience reasoning about inference performance and tradeoffs.
- Ability to combine scientific judgment with a practical approach to shipping software.
- Comfort working with researchers and engineers on open-ended problems.
- Fluency in using AI agents to move quickly across workstreams.
- Experience with model distillation, representation learning, calibration, or efficient inference is a plus.