EngineeringOn-site

Software Engineer, Inference - Multi Modal

OpenAI · On-site

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Last seen by MeritLog September 12, 2026Source: AshbySource version: ashby-public-job-posting-v1

MeritLog read this listing from OpenAI's Ashby job board and last checked it on September 12, 2026.

Source: the employer's Ashby job board. Open the original listing for current details.

Job details

Work model: On-site
Salary: $295K - $555K
Location: San Francisco
Occupation: Software Developers(O*NET 15-1252.00)

Hiring context

How this role compares at OpenAI

OpenAI has 797 live roles in MeritLog’s catalog across 14 job families, and 307 of them are in engineering. 664 of those listings publish a pay range, a disclosure rate of 83%.

This role's posted range of $295K - $555K sits above 91% of the 651 other OpenAI roles quoted over the same currency and period.

OpenAI concentrates this hiring in:

Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.

What the role asks for

What you'd do

Design and implement inference infrastructure for large-scale multimodal models.
Optimize systems for high-throughput, low-latency delivery of image and audio inputs and outputs.
Enable experimental research workflows to transition into reliable production services.
Collaborate closely with researchers, infra teams, and product engineers to deploy state-of-the-art capabilities.
Contribute to system-level improvements including GPU utilization, tensor parallelism, and hardware abstraction layers.

What they're asking for

Have experience building and scaling inference systems for LLMs or multimodal models.Skill
Have worked with GPU-based ML workloads and understand the performance dynamics of large models, especially with complex data like images or audio.Skill
Enjoy experimental, fast-evolving work and collaborating closely with research.Skill
Are comfortable dealing with systems that span networking, distributed compute, and high-throughput data handling.Skill
Have familiarity with inference tooling like vLLM, TensorRT-LLM, or custom model parallel systems.Skill
Own problems end-to-end and are excited to operate in ambiguous, fast-moving spaces.Skill
Experience working with image generation or audio synthesis models in production.SkillPreferred
Exposure to distributed ML training or system-efficient model design.SkillPreferred

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job description

About the Team OpenAI’s Inference team powers the deployment of our most advanced models - including our GPT models, 4o Image Generation, and Whisper - across a variety of platforms. Our work ensures these models are available, performant, and scalable in production, and we partner closely with Research to bring the next generation of models into the world. We're a small, fast-moving team of engineers focused on delivering a world-class developer experience while pushing the boundaries of what AI can do. We’re expanding into multimodal inference, building the infrastructure needed to serve models that handle image, audio, and other non-text modalities. These workloads are inherently more heterogeneous and experimental, involving diverse model sizes and interactions, more complex input/output formats, and tighter coordination with product and research. About the Role We’re looking for a software engineer to help us serve OpenAI’s multimodal models at scale. You’ll be part of a small team responsible for building reliable, high-performance infrastructure for serving real-time audio, image, and other MM workloads in production. This work is inherently cross-functional: you’ll collaborate directly with researchers training these models and with product teams defining new modalities of interaction. You'll build and optimize the systems that let users generate speech, understand images, and interact with models in ways far beyond text. In this role, you will: - Design and implement inference infrastructure for large-scale multimodal models. - Optimize systems for high-throughput, low-latency delivery of image and audio inputs and outputs. - Enable experimental research workflows to transition into reliable production services. - Collaborate closely with researchers, infra teams, and product engineers to deploy state-of-the-art capabilities. - Contribute to system-level improvements including GPU utilization, tensor parallelism, and hardware abstraction layers. You might thrive in this role if you: - Have experience building and scaling inference systems for LLMs or multimodal models. - Have worked with GPU-based ML workloads and understand the performance dynamics of large models, especially with complex data like images or audio. - Enjoy experimental, fast-evolving work and collaborating closely with research. - Are comfortable dealing with systems that span networking, distributed compute, and high-throughput data handling. - Have familiarity with inference tooling like vLLM, TensorRT-LLM, or custom model parallel systems. - Own problems end-to-end and are excited to operate in ambiguous, fast-moving spaces. Nice to Have: - Experience working with image generation or audio synthesis models in production. - Exposure to distributed ML training or system-efficient model design. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form https://form.asana.com/?d=57018692298241&k=5MqR40fZd7jlxVUh5J-UeA. No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link https://form.asana.com/?k=bQ7w9h3iexRlicUdWRiwvg&d=57018692298241. OpenAI Global Applicant Privacy Policy https://cdn.openai.com/policies/global-employee-and-contractor-privacy-policy.pdf At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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