About the company
Our client is a software company.
The role
Raydar is recruiting for this role on behalf of our client. Join as an early technical hire on a small team that blends research with production engineering. You will design measurement tooling and data workflows for machine learning models, run and study training experiments, and convert promising results into dependable shared platform components. The role involves close collaboration with the founders.
What you'll do
- Create assessment frameworks, scoring tools and data workflows drawn from real-world usage records.
- Take charge of model training efforts in live settings, covering data preparation, assessment and training runs, with the aim of improving quality, cost and latency.
- Develop simulated environments and test harnesses that make experiments repeatable and generate training data.
- Carry out and interpret experiments, replicate promising published work and troubleshoot the surrounding systems.
- Harden successful experiments into dependable platform components used by colleagues and customers.
Requirements
What we're looking for
- Solid coding ability, with Python preferred, plus a firm grounding in machine learning and computer science fundamentals.
- 2 to 4 years as a machine learning engineer, research engineer or similar at an AI-focused startup or lab, or an exceptional early-career engineer with research lab experience and clear product sense.
- A significant piece of work, such as a project, published research or a production system, that shows depth and a strong sense of ownership.
- A track record of exploring how models are refined or served, shown through experiments, publications, open-source contributions or startup work.
- Quick at getting up to speed on new codebases, comfortable debugging alone and able to communicate technical choices plainly.
- A habit of verifying results and seeing work through to completion.
- Degree from a top 25 university.
- Ability to work on-site five days a week.
Bonus points
- Public technical writing such as research papers or blog posts.
- Experience spanning several layers of the machine learning stack, for example evaluation and training, or training and serving.
- Hands-on experience with PyTorch, large language model training, evaluation harnesses or model serving.
Benefits
Compensation and benefits
- Base salary: USD 200,000 to 300,000 per year
- Equity
Location and work model
- Mountain View, CA, United States
- On-site, 5 days per week in office
- Full-time
Skills Required
- Solid coding ability, with Python preferred
- Strong grounding in machine learning and computer science fundamentals
- 2 to 4 years of experience as a machine learning engineer, research engineer, or similar at an AI-focused startup or lab, or exceptional early-career engineering experience with research lab experience and product sense
- Significant project, published research, or production system demonstrating depth and ownership
- Experience exploring how models are refined or served through experiments, publications, open-source contributions, or startup work
- Ability to learn new codebases quickly, debug independently, and communicate technical choices clearly
- Track record of verifying results and completing work
- Degree from a top 25 university
- Ability to work on-site five days per week
- Public technical writing, such as research papers or blog posts
- Experience across multiple layers of the machine learning stack, such as evaluation and training or training and serving
- Hands-on experience with PyTorch, large language model training, evaluation harnesses, or model serving
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.
