About the company
Our client is a fast-growing artificial intelligence company.
The role
Raydar is recruiting for this role on behalf of our client. Drive the development of compact machine learning models built to run on consumer hardware. The work spans the full model lifecycle, from preparing data and choosing training methods to validating results and getting finished models into production, with a strong emphasis on running experiments and delivering working outcomes.
What you'll do
- Lead the training effort for a group of small models, including decisions on early-stage training, later refinement and model compression.
- Take responsibility for individual model capabilities from the initial data choices through testing and release.
- Plan experiments and document findings clearly so the wider team can build on them, dropping weak ideas quickly and investing further in promising ones.
- Run a focused stream of training work tied to a measurable goal and deliver a model build that goes into production.
- Collaborate with software engineers, partnership colleagues and other researchers so research results reach users and shared code stays easy to read.
- Early on, reproduce a recent training run and propose the most valuable research directions, then deliver a model build that outperforms the current best and help shape the following training cycle.
Requirements
What we're looking for
- Strong hands-on background in modern model training, including both initial training and later refinement with fine-tuning and reinforcement learning methods.
- Practical experience with model compression and other techniques for making small models more capable.
- Experience in reinforcement learning and/or running models directly on local devices.
- Comfort with systems topics such as memory management and GPU performance tuning.
- Skill at designing experiments that move a meaningful measure of quality.
- A history of owning work from start to finish and delivering working models rather than only publications.
- High initiative, a sense of urgency and the ability to move quickly.
- Exposure to rapid growth at a leading AI lab or at a startup as a founder or early employee; relevant skills matter more than credentials.
Bonus points
- Published research in reinforcement learning, on-device machine learning or efficient inference.
- Track record of delivering models into production environments against tight hardware timelines.
- Experience with machine learning at consumer scale.
Benefits
Compensation and benefits
- Base salary: USD 200,000 to 250,000 per year
- Equity
- Relocation support
Location and work model
- San Francisco, CA, United States
- On-site
- Full-time
Skills Required
- Strong hands-on experience with modern model training, including initial training, fine-tuning, and reinforcement learning methods
- Practical experience with model compression and techniques for improving small-model capabilities
- Experience with reinforcement learning and/or running models directly on local devices
- Understanding of memory management and GPU performance tuning
- Ability to design experiments that improve meaningful quality metrics
- Track record of owning work from start to finish and delivering working models
- High initiative, urgency, and ability to move quickly
- Exposure to rapid growth at a leading AI lab or experience as a startup founder or early employee
- Published research in reinforcement learning, on-device machine learning, or efficient inference
- Experience delivering models into production under tight hardware timelines
- Experience with machine learning at consumer scale
What We Do
Raydar is a talent acquisition and business consulting firm that connects world-class and emerging talent with growing organizations. It supports companies through team development, strategic hiring, and customized growth solutions, helping clients recruit roles such as engineers, product managers, executives, legal counsel, and quantitative traders. Raydar focuses on understanding each organization’s needs, culture, and long-term goals to build high-impact teams.
