This free Applied Scientist job description template is ready to use — copy it, replace the {{placeholders}}, and post your role in minutes. It includes a company intro, a role summary, responsibilities, requirements, nice-to-haves, and compensation, with writing tips and FAQs below to help you tailor it to your team.
When to use this template
Use this when you're hiring someone to apply machine learning research to real products — bridging the gap between cutting-edge methods and shipped features. Applied scientists combine research depth with the engineering chops to make things real.
Applied scientist candidates want to know the problems, the data, and how close to production the role sits. Be specific about the balance of research and engineering, which varies a lot by company.
If the role is pure novel research, use the Research Scientist template; if it's mostly production engineering, use the Machine Learning Engineer template.
Writing tips
- Describe the problems and data, and how close to production the role sits.
- Clarify the balance of research depth vs. engineering.
- Distinguish from pure research and pure ML engineering roles.
- Emphasize translating methods into measurable product impact.
- Include the salary range and seniority level.
The job description
Copy the template below and replace the {{placeholders}} and [bracketed notes] with your specifics.
About {{company}}
{{company}} is [what you do]. We have rich problems and data in [area], and we're hiring an Applied Scientist to turn the latest methods into real product impact.
The role
As an Applied Scientist, you'll bridge research and production — developing and adapting ML methods, prototyping quickly, and working with engineers to ship them. You'll care about both rigor and real-world impact. This role reports to {{hiring_manager}} and is based {{work_type}} in {{location}}.
What you'll do
- Apply and adapt ML methods to real product problems in [area].
- Prototype quickly, then work with engineers to productionize what works.
- Design experiments and evaluate models rigorously.
- Stay current with research and bring the best ideas in.
- Measure and communicate the impact of your work.
What we're looking for
- An advanced degree in a relevant field, or equivalent experience.
- Strong ML foundations and the engineering skills to prototype.
- A track record of applying research to real, shipped impact.
- Rigor with experiments and evaluation.
- Clear communication with engineers, product, and leadership.
Nice to have
- Experience taking models to production.
- Publications or applied research in [your area].
- Familiarity with modern ML and MLOps tooling.
What we offer
- Salary range: {{salary_range}}, plus equity.
- [Comprehensive benefits].
- Flexible {{work_type}} working and [PTO policy].
- The satisfaction of seeing your research reach real users.
How to personalize
Replace these placeholders before posting:
- {{company}}
- {{location}}
- {{work_type}}
- {{salary_range}}
- {{hiring_manager}}
The bracketed notes — like [your benefits] or [your primary language(s)] — are prompts to swap in your own details. The more specific you are about the actual work and stack, the stronger your applicant pool will be.
Frequently asked questions
- What does an Applied Scientist do?
- An Applied Scientist applies machine learning research to real products, bridging science and engineering. They develop and adapt methods, prototype quickly, design and evaluate experiments, and work with engineers to ship models — balancing rigor with measurable product impact.
- What's the difference between an Applied Scientist and a Data Scientist?
- An Applied Scientist focuses on developing and applying ML methods, often with deeper research training and stronger engineering skills. A Data Scientist focuses more on analysis, experimentation, and insight for the business. The roles overlap, but applied scientists sit closer to building models that ship.
- What skills should an Applied Scientist have?
- Strong ML foundations, the engineering skills to prototype and help productionize models, rigor with experiments and evaluation, and a track record of turning research into real impact. An advanced degree is common but a record of applied results matters most.