Camera ISP Software Engineer, Consumer Devices

Location

San Francisco

Employment Type

Full time

Department

Consumer Products

Compensation

• $293K – $325K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The Consumer Products team at OpenAI builds and ships end-to-end hardware and software experiences that bring frontier AI into the physical world. We work across industrial design, hardware engineering, embedded systems, and AI research to create category-defining consumer devices that feel intuitive, reliable, and deeply integrated with OpenAI’s models.

Within this organisation, the Camera team is responsible for delivering robust, production-ready imaging systems that operate under real-world constraints such as motion, power, latency, and manufacturing variation. Our work spans early silicon and board bring-up through mass production, and directly supports both user-facing experiences and on-device perception for AI-driven features.

We operate as a small, highly technical team with deep ownership, close cross-functional collaboration, and a strong bias toward shipping high-quality products rather than incremental demos.

About the Role

As a Camera ISP Software Engineer, you will own end-to-end ISP bring-up and tuning across prototype and production hardware. You’ll work across software tuning and hardware-accelerated pipelines to deliver stable, high-quality imaging under real-world constraints such as motion, power, and manufacturing variation.

This role is ideal for engineers who thrive in ambiguous early-stage environments, enjoy deep technical ownership, and take pride in shipping production-grade imaging systems.

This role is based in San Francisco, CA. We use a hybrid work model of four days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Own end-to-end ISP tuning and bring-up, from early prototypes through production hardware.

• Tune core 3A and image-quality blocks including AE, AWB, AF, noise reduction (spatial and temporal), sharpening, tone mapping, color correction, HDR/WDR, flicker mitigation, lens shading, defect pixel correction, and related ISP stages.

• Build repeatable capture and evaluation workflows, combining controlled lab sweeps with real-world validation.

• Validate robustness across sensor, module, and manufacturing variation with clear, defensible success criteria.

• Deliver production-grade tuning artifacts, including versioned tuning packs, parameter manifests, change logs, and curated RAW and processed datasets with supporting documentation.

• Partner closely with camera firmware, systems, and hardware teams to debug pipelines, configure sensors, and resolve board-level and silicon-level issues as designs evolve.

You might thrive in this role if you:

• Deep experience shipping ISP tuning for embedded or mobile camera products, from bring-up through production.

• Strong fundamentals across 3A (AE/AWB/AF), noise modeling, sharpening and detail trade-offs, tone mapping, and color pipelines.

• Proven ability to run data-driven tuning loops, including repeatable capture, rigorous evaluation, and disciplined versioning of tuning artifacts.

• Hands-on experience debugging end-to-end camera pipelines on real hardware, including RAW capture, processed outputs, and hardware-accelerated paths.

• Ability to collaborate cross-functionally and translate system-level constraints (power, latency, motion, reliability) into concrete tuning decisions and validation plans.

Preferred Qualifications

• Experience with common mobile or embedded SoC camera stacks and vendor frameworks.

• Experience tuning imaging pipelines explicitly for downstream machine-learning consumers, with iteration driven by model feedback.

• Experience designing motion-aware temporal imaging strategies that remain stable under rapid device or scene motion.

Experience Level

Mid

Employment Type

Full time

Workplace Type

Hybrid

Category

Engineering

Industry

Technology

Salary Range

$293K – $325K

Required Skills

• Camera ISP tuning
• Embedded systems
• AI research
• Industrial design
• Hardware engineering
• Machine learning
• Image processing
• Computer vision

Preferred Skills

• Mobile or embedded SoC camera stacks
• Vendor frameworks
• Imaging pipelines
• Motion-aware temporal imaging strategies
• Downstream machine-learning consumers
• Model feedback
• System-level constraints
• Power
• Latency
• Motion
• Reliability

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