The Google DeepMind Polaris Research Engineer vacancy is based in Mountain View, California. The team builds realistic tasks that evaluate Gemini’s ability to carry out complex software engineering work. Applicants need practical programming experience and familiarity with AI-assisted development and coding agents.
Google Careers gives a US base salary range of $164,000–$252,000. It also describes a separate total compensation range that includes bonus, equity and sign-on components, subject to individual terms. This guide explains the work, qualifications, application route and the distinction between base salary and total compensation.
Google DeepMind Polaris Research Engineer Details
Polaris designs examinations of model performance that reflect the complexity of real development projects. A task includes a prompt describing the work and a grader or rubric that assigns a score to the attempted solution.
The team prioritises a smaller number of carefully constructed tasks rather than relying entirely on high-volume synthetic generation. Google says a well-developed task typically takes a Polaris engineer roughly a week from the initial idea to final approval, making technical judgement central to the role.
Employer | Google DeepMind |
|---|---|
Role | Research Engineer, Polaris |
Location | Mountain View, CA, United States |
Focus | Long-horizon software engineering tasks and Gemini evaluations |
US base salary | $164,000–$252,000 |
Advertised total compensation | $550,000–$1,100,000 including 15% target annual bonus, equity and sign-on bonus |
Closing date | Not announced |
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Salary and Employee Benefits
The Google DeepMind Polaris Research Engineer listing separates base pay from overall compensation. $164,000–$252,000 is the base salary range; the larger $550,000–$1,100,000 range includes a 15% target annual bonus, equity and a sign-on bonus.
Individual pay depends on job-related skills, experience and relevant education or training. Equity awards are discretionary and subject to approval and grant terms, so the advertised range should not be presented as guaranteed cash salary for every successful applicant.
Qualifications and Technical Fit
The minimum qualifications focus on experience in software development or programming, AI-assisted coding tools and coding agents. Google does not state a PhD requirement or a fixed number of years of experience in the minimum qualifications.
Preferred experience includes competitive programming or mathematics competitions, hackathons, evaluation frameworks and benchmark datasets. The team also values the ability to learn new tools, programming approaches and technical stacks quickly.
Responsibilities and Evidence to Prepare
The advertised responsibility is to create tasks measuring Gemini’s ability to complete long-horizon software engineering work. The role also involves launching projects that improve DeepMind’s training data and evaluations.
As practical preparation, choose examples that demonstrate how you define a problem and judge whether a solution is correct. A project portfolio can help explain these skills, but Google Careers does not list a GitHub link, competition medal or separate research proposal as a mandatory document.
How to Apply for the Google DeepMind Polaris Research Engineer Role
Open the official Google Careers vacancy and use its Apply action. Confirm the job title, Mountain View location and current qualifications before uploading your application, since vacancies can be edited or removed.
Google does not state remote eligibility for every country or automatic visa sponsorship. Candidates with location or immigration constraints should clarify these through the recruitment process and avoid assuming that a US job is globally remote.
Application Deadline and Related Google Opportunities
Google Careers does not publish a closing date for this role. An open Apply button does not guarantee how long recruitment will continue, so apply as early as you can.
To explore other roles at the company, see our Google Jobs guide; requirements and locations differ by role.
Frequently Asked Questions
The Google DeepMind Polaris Research Engineer questions below focus on practical application decisions. These answers clarify the qualifications and compensation stated in the vacancy. They also separate useful preparation from documents explicitly required by the application form.
Check the official listing before applying because role details and availability can change. Recruitment-specific questions should be directed through Google’s application process.
Where is this role based?
Google lists Mountain View, California, United States. The vacancy does not establish a universal remote-work arrangement.
Does the role require a PhD?
Google’s minimum qualifications do not specify a PhD. They focus on programming and experience with AI-assisted development and coding agents.
Is $1.1 million the base salary?
No. The upper total compensation figure includes bonus, equity and sign-on components. The published base salary range is $164,000–$252,000.
What does a Polaris evaluation task contain?
A task combines a prompt for a complex software engineering scenario with a grader or rubric. That scoring mechanism evaluates the quality of the model’s attempted solution.
Is there a confirmed closing date?
Google has not published a closing date for this role. Apply through the Google Careers page and check that recruitment remains open.
Conclusion
The Google DeepMind Polaris Research Engineer role suits developers who can combine implementation skills with careful evaluation of agent-assisted work. The official description emphasises realistic tasks, robust scoring and adapting to unfamiliar technical problems. Demonstrate those abilities with concrete experience rather than relying on broad interest in AI.
Use the current $164,000–$252,000 base salary range when assessing the opportunity and review the additional compensation terms separately. Check the official vacancy for current availability, application requirements and location details. Submit through Google Careers and retain a copy of the role information you used.
