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Staff Backend Software Engineer (Product)

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Onsite

Staff Backend Software Engineer (Product)

Otter.ai

Staff Backend Software Engineer (Product)

Skills

AI

1 month ago

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Important information


Contract type:

Permanent

Salary:

$210,000 – $250,000 / year

Location:

Mountain View, CA, USA

Work mode:

Onsite

Published on:

17 September 2026

What they need


  • As a Staff Software Engineer on our Product teams, you’ll define how these capabilities are translated into scalable systems and cohesive product experiences
  • You’ll operate across the full lifecycle of AI-driven features—from data ingestion and model orchestration to output structuring and delivery into user-facing product surfaces—but at a systems level. This includes shaping the architecture behind generation pipelines, defining how AI outputs are structured and persisted, and ensuring these systems are reliable, performant, and adaptable as both product requirements and underlying models evolve
  • This role goes beyond individual features. You’ll drive technical direction across product areas, aligning product, AI, and infrastructure teams on how to build AI-native systems that scale. You’ll help define patterns for how we evaluate, iterate on, and continuously improve AI-driven experiences, turning ambiguous opportunities into clear systems and execution plans
  • You’ll partner closely with product, design, and AI teams to identify high-leverage opportunities, prototype new approaches, and guide them into production—shaping both the product experience and the underlying systems that power it
  • Define system architecture for AI-native product surfaces
  • Design foundational systems (generation pipelines, data models, evaluation frameworks) that power multiple product areas
  • Drive cross-team technical strategy
  • Lead initiatives that span product, AI, and infrastructure teams, ensuring alignment on architecture and execution
  • Establish best practices for AI product engineering
  • Set standards for how we build with AI—covering areas like evaluation, reliability, iteration loops, and system design
  • Translate ambiguity into scalable systems and product direction
  • Take loosely defined opportunities and turn them into clear technical approaches and execution plans
  • Raise the bar across the organization
  • Influence engineering quality, system design, and product thinking beyond your immediate team
  • Shape how AI accelerates engineering velocity
  • Drive adoption and best practices for AI-assisted development, improving how teams design, build, and iterate on product

Benefits

  • Compensation: With highly competitive salaries and equity option packages, everyone’s an owner here.
  • Hybrid Working: You can mix and match working from our Mountain View or San Francisco office, plus a couple days from home.
  • Social Gatherings: Quarterly gatherings in the Bay Area for some team real fun and authentic connection.
  • Health and Wellness: Comprehensive medical, dental, vision, life insurance, disability, and wellness benefits that start the first day you do.
  • PTO: Start with a minimum of 15 days of paid time off, plus up to 18 weeks for maternity leave and 8 weeks for paternity leave.
  • 401(k): Plans to help meet your retirement goals because we care about your future.- Has a strong track record of owning technical and product direction for large or ambiguous problem spaces
  • Is comfortable operating at a high level of ambiguity and defining both the problem and the solution
  • Has 10+ years of experience building and scaling complex systems
  • Has experience leading cross-team initiatives and influencing without direct authority
  • Demonstrates deep expertise in system design, especially in data-intensive or distributed systems
  • Brings strong product intuition and can balance user experience, system constraints, and business impact
  • Experience designing systems that balance latency, cost, and quality
  • Exposure to user feedback loops, experimentation, or evaluation frameworks
  • Experience building or experimenting with LLM-powered features (prompting, evaluation, RAG, etc.)

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