Senior Staff Machine Learning Engineer – Media Intelligence

Adobe

Senior Staff ML Engineer architecting search and data infrastructure for Adobe’s Firefly Foundry generative AI service. Building scalable multimodal retrieval and agentic search over customer media.

Posted 8/27/2026full-timeSan Jose • California, New York, Washington • 🇺🇸 United StatesSenior💰 $190,200 - $345,650 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in designing and building scalable data-processing pipelines and search systems, with a strong focus on vector retrieval, ranking, and multimodal search capabilities. Proven ability to lead technical direction, mentor teams, and ensure high performance and quality in enterprise-scale data environments.

Highest-signal resume keywords

Machine Learning ExpertiseVector/ANN RetrievalLarge-Scale Data ProcessingTechnical LeadershipPython Programming

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Processing PipelinesSearch and Retrieval SystemsRanking and RerankingData ModelingEmbedding ModelsObservability and MonitoringMulti-Tenant SystemsBatch and Streaming PipelinesQuery UnderstandingProvenance Evaluation

Soft Skills

Excellent CommunicationData-Driven Problem-SolvingMentoring

Tools & Technologies

DockerKubernetesAWSAzurePyTorchCI/CD

Certifications & Qualifications

MS or PhD in Computer ScienceComputer Engineering

Industry Keywords

Hybrid RetrievalMultimodal SearchCross-Modal SearchData ResidencyAccess Controls

Tech Stack

Tools & technologies

AWSAzureDockerGoKubernetesPythonPyTorchRust

About the role

Key responsibilities & impact

Design and build scalable data-processing pipelines transforming customer media and model-derived signals into structured, searchable intelligence
Contribute to the technical vision and architecture for Firefly Foundry’s media-intelligence data platform and search stack
Architect indexing and search infrastructure for hybrid lexical and vector retrieval, multimodal and cross-modal search, ranking, reranking, faceting, and metadata filtering
Build agentic search capabilities including tool/function-call retrieval interfaces, multi-hop query planning, iterative retrieval, grounded results, citations, and provenance
Own index lifecycle and freshness through incremental and streaming indexing, backfills, reprocessing, and schema and embedding-model versioning
Engineer enterprise capabilities including per-tenant index isolation, data residency, and access controls
Define and enforce retrieval quality gates, offline and online evaluation, regression detection, and drift monitoring
Own platform performance and cost, including latency and throughput SLAs, ANN tuning, GPU-accelerated enrichment, and infrastructure right-sizing
Build deployment, observability, monitoring, and alerting across data and search systems
Operate systems at enterprise scale through on-call, incident response, and postmortems
Lead technically across teams, set standards, drive build/buy and design decisions, mentor senior engineers, and represent architecture to leadership and partner organizations
Partner with Applied Science, agent and product teams, ML Engineering leadership, AI Platform, and Firefly Foundry Studio

Requirements

What you’ll need

10+ years in machine learning, data, or infrastructure engineering
Deep ownership of large-scale data processing and/or search and retrieval systems in production
Track record of leading systems and setting technical direction across teams
Deep expertise in vector/ANN retrieval, lexical search, hybrid retrieval, ranking and reranking, and query understanding
Experience with large-scale batch and streaming pipelines, data modeling, object stores, vector databases, and columnar/OLAP systems
Experience building retrieval for LLM and agentic systems, including RAG, multimodal and cross-modal search, grounding, provenance, and retrieval evaluation
Strong Python; systems language such as Go, Rust, or C++ is a plus
Hands-on familiarity with embedding models and inference paths, including PyTorch
Experience with observability, monitoring, and alerting for data and search systems
Experience with multi-tenant systems and data isolation in enterprise or regulated contexts
Fluency with Docker, Kubernetes, CI/CD, and AWS or Azure
Comfort evaluating retrieval quality across text, image, video, 3D, and audio modalities
Proven technical leadership, mentoring, cross-organizational design and build/buy decisions, and roadmap influence
Excellent communication and data-driven problem-solving
MS or PhD in Computer Science, Computer Engineering, or related field, or equivalent practical experience

Benefits

Comp & perks

Annual Incentive Plan (AIP) for non-sales roles
Potential new hire equity award for eligible roles
Comprehensive benefits programs
Equal Employment Opportunity protections
Disability accommodations during the recruiting process