Ridgeline

Ridgeline

Staff Software Engineer, Investment Performance and Analytics

Reno, NV · Staff+

No sponsorship$153k-$191kDetected 19 days ago
KotlinGitAWSAzureAccountingRecruitingHRISTest AutomationLeadershipCollaborationProblem Solving

About the role

  • Do you enjoy solving complex quantitative challenges involving large datasets, financial calculations, and analytics infrastructure?
  • Are you looking for an opportunity to help shape the next generation of investment performance and analytics technology?
  • This role sits at the intersection of software engineering, quantitative financial analytics, and investment technology.

Compensation

  • The typical starting salary range for new hires in this role is $153,000 - $191,000.
  • Final compensation amounts are determined by multiple factors, including candidate experience and expertise and may vary from the amount listed above.
  • As an employee at Ridgeline, you'll have many opportunities for advancement in your career and can make a true impact on the product.
  • In addition to the base salary, Ridgeline employees can participate in our Company Stock Plan subject to the applicable Stock Option Agreement.
  • Are you passionate about building highly accurate, calculation-intensive financial systems where correctness, scale, and transparency are critical?
  • Do you enjoy solving complex quantitative challenges involving large datasets, financial calculations, and analytics infrastructure?

Benefits

  • These include unlimited vacation, educational and wellness reimbursements, and $0 cost employee insurance plans.
  • Please check out our Careers page for a more comprehensive overview of our perks and benefits.
  • By joining Ridgeline, you'll help redefine investment management technology while working alongside a team committed to collaboration, learning, and technical excellence.

Visa & Work Authorization

  • You must be authorized to work in the United States without sponsorship.

This listing is sourced directly from Ridgeline's careers page and normalized into a canonical job model.