Firefly Foundryis Adobes enterprise managed-service offering for custom multimedia generative AI deep-tuned image video and 3D models built on each customers 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 Foundrys 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

• 5years 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 data-driven problem-solving and excellent communication in cross-functional teams.

Education

• Masters or PhD in Computer Science Computer Engineering or a related field or equivalent practical experience building and operating production ML systems.