Senior Staff Machine Learning Engineer – Media Intelligence

Jobtailor; California (MO); On-site; USD 150,000 - 190,000; Full time

Job summary

Firefly Foundry is seeking an experienced senior engineer to design and oversee scalable data-processing pipelines and a hybrid search stack. You will drive architecture for vector and lexical retrieval, enforce data governance, and partner with ML and product teams to deliver enterprise-grade intelligence.

We value deep ownership, strong Python skills, and experience with multi-tenant, observability-focused platforms. Remote-friendly with enterprise-scale data challenges.

Qualifications

• 10+ years in machine learning, data, or infrastructure engineering.
• Deep ownership of large-scale data processing and/or search systems in production.
• Track record of leading systems and setting technical direction across teams.

Responsibilities

• Design and build scalable data-processing pipelines transforming media and signals into structured intel.
• Architect indexing and search infrastructure for hybrid lexical and vector retrieval.
• Own platform performance, latency, and cost with SLAs and tuning.
• Lead technically across teams, mentor engineers, and set architectural standards.
• Collaborate with ML, product, and platform teams to drive roadmaps.
• Build deployment, observability, and monitoring across data and search systems.

Skills

Vector/ANN Retrieval

Large-Scale Data Processing

Python Programming

Go/Rust/C++

Docker

Kubernetes

AWS/Azure

ML Engineering

Search and Retrieval Systems

Mentoring/Technical Leadership

Education

MS or PhD in Computer Science or Computer Engineering

Tools

PyTorch

CI/CD

AWS

Azure

Vector databases

Job description

• 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

• 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

Core Competencies

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 Expertise
• Vector/ANN Retrieval
• Large-Scale Data Processing
• Technical Leadership
• Python Programming

Hard Skills

• Data Processing Pipelines
• Search and Retrieval Systems
• Ranking and Reranking
• Data Modeling
• Embedding Models
• Observability and Monitoring
• Multi-Tenant Systems
• Batch and Streaming Pipelines
• Query Understanding
• Provenance Evaluation

Soft Skills

• Excellent Communication
• Data-Driven Problem-Solving
• Mentoring

Certifications & Qualifications

• MS or PhD in Computer Science
• Computer Engineering

Industry Keywords

• Hybrid Retrieval
• Multimodal Search
• Cross-Modal Search
• Data Residency
• Access Controls

Tools & Technologies

• Docker
• Kubernetes
• AWS
• Azure
• PyTorch
• CI/CD