Firefly Foundryis Adobe’s enterprise managed-service offering for custom multimedia generative AI — deep-tuned image, video, and 3D models built on each customer’s IP, paired with creative production workflows and a media-intelligence layer, and deployed across new and existing Adobe surfaces. The business has gained significant traction in Media & Entertainment, marketing, and consumer retail, and is expanding rapidly into adjacent verticals.

We are hiring aSenior Machine Learning Engineerto build the pipelines and services that turn Firefly Foundry’s models into reliable, enterprise-grade products. You will compose heterogeneous model pipelinesincludingfinetunedLLMs, image and video generationmodels, 3D mesh reconstruction, upsamplers, NSFW and safety checkers, and IP guardrail models— deploy them as services, scale those services to enterprise traffic, and hold them to SLAs for availability and latency, all while ensuring served quality matches the training and reference environment. Across this work you will integrate and operate multiple, distinct generative model architectures, in a mix that evolves quickly.

This is a high-ownership role in a fast-moving environment, with direct, measurable impact on the availability, latency, cost, and quality of everything Firefly Foundry ships. Depending on your focus area, you may own externalizable data pipelines for self-serve fine-tuning, optimized VLM deployments for media intelligence and querying, or the platform that lets the team deploy new pipelines rapidly with full observability.

What you will do

• Own the full serving lifecycle for heterogeneous model pipelines — packaging, versioned rollout, canary/rollback, and autoscaling — from research checkpoint to enterprise endpoint.

• Deploy these pipelines as services, scale them to enterprise traffic, and hold them to SLAs for availability, latency, and throughput.

• Ensure served quality matches the training and reference environment — closing train/serve gaps across precision, preprocessing, and model versions.

• Engineer for enterprise from the ground up: tenancy boundaries, data isolation, and the controls that let us honor customer IP contracts under audit.

• Build the platform underneath it all — rapid pipeline deployment, observability, monitoring, and alerting.

• Define and enforce quality gates in the deployment pipeline – automated eval, regression detection, and drift monitoring that block bad model versions from reaching production.

• Own GPU capacity and cost –utilization, batching efficiency, and right-sizing acceleration fleets against latency SLAs.

• Run production ML operationally – on-call, incident response, an dpostmortems for availability and latency regressions

Depending on your focus area, you may also:

• Build externalizable data pipelines that power self-serve fine-tuning flows for enterprise customers.

• Stand up optimized VLM deployments for media intelligence and content querying.

Who you will partner with

• Applied Science— to take research models into reliable, high-throughput serving and to keep served quality faithful to the training environment.

• ML Engineering leadership and AI Platform— on shared infrastructure, accelerator capacity, and serving primitives at platform scale.

• Firefly Foundry Studio— to translate creative production workflows into performant, dependable ML services.

What you bring

• 5+years inmachine learning engineering, with significant ownership of production ML or inference services at scale.

• Strong Python and deep-learning engineering skills (PyTorch), with hands-on experience deploying and scaling model-backed services.

• Experience composing multi-model pipelines and serving them behind APIs — orchestration, batching, autoscaling, and version management.

• A track record owning production SLAs — availability, latency, and throughput — backed by real observability, monitoring, and alerting.

• Comfort working across multiple, distinct generative model architectures (LLMs and VLMs, diffusion and transformer models, 3D/mesh) — enough to integrate, optimize, and reason about output quality, in partnership with Applied Science.

• Experience with multi-tenant systems and data isolation in an enterprise or regulated context.

• Fluency with containers and orchestration (Docker, Kubernetes), CI/CD for ML, and a major cloud (AWS or Azure).

• GPU inference optimization for latency and cost — quantization, batching, and serving runtimes; custom CUDA a plus.

• Strong, href="https://www.adobe.com/adobe-for-all.html#:~:text=and%20culture.-,Our%20company%20values.,-As%20we%20look">values and culture, focus on people, purpose and community, Adobe for All, comprehensive benefits programs, the stories we tell, the customers we serve, and how you can help us advance our mission of empowering everyone to create.

  Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. Learn more.

  Adobe aims to make our Careers website and recruiting process accessible to any and all users. If you have a disability or special need that requires accommodation to navigate our website or complete the application process, email accommodations@adobe.com or call +1 408-536-3015.

  AI Use Guidelines for Interviews:
  Our interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process.

  At Adobe, we empower employees to innovate with AI — and we look for candidates eager to do the same. As part of the hiring experience, we provide clear guidance on where AI is encouraged during the process and where it’s restricted during live interviews. See how we think about AI in the hiring experience.

  Expected Pay Range:

  Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $151,800 -- $265,350 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process. In California, the pay range for this position is $183,300 - $265,350 In Washington, the pay range for this position is $165,600 - $239,725

  At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).

  In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.

  State-Specific Notices:

  California:

  Fair Chance Ordinances

  Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.

  Colorado:

  Application Window Notice

  There is no deadline to apply to this job posting because Adobe accepts applications for this role on an ongoing basis. The posting will remain open based on hiring needs and position availability.

  Massachusetts:

  Massachusetts Legal Notice

  It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.