Machine Learning Engineer; GoLang

Make your mark at Comcast -- a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award-winning technology team that turns big ideas into cutting‑edge products, platforms, and solutions that our customers love. We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace.

Join us. You’ll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on‑site collaborating unless the team has been designated as virtual due to the nature of their work. If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.)

Multimodal Analysis Framework (MAF) is an end‑to‑end platform designed to process diverse content sources—including video, images, audio, and documents
—to generate rich, structured metadata. The platform unifies multiple ML/AI models to extract curated insights at scale, tailored to specific business needs. MAF supports both on‑demand workloads (batch uploads, ad‑hoc analysis) and real‑time streaming workflows, enabling continuous metadata generation for live content streams. Customers can define their metadata requirements—such as entity extraction, scene segmentation, object detection, transcription, summarization, or multimodal correlation—and the framework orchestrates the appropriate models and tool chains to deliver high‑quality outputs.

Through flexible APIs and UI‑based workflows, customers and internal teams can visualize metadata, trigger enrichment, monitor processing, and integrate results into downstream applications. The platform emphasizes modularity, scalability, and extensibility to support new ML models, LLM‑based agents, and cross‑modal inference as use cases evolve. We are looking for a mid‑level Backend Engineer to join our Machine Learning Platform team
. This role focuses on building scalable backend systems that power ML workloads, including video, image, and document processing
, and enable LLM‑driven applications through agents and MCP servers
. You will work primarily in Golang
, deploy and operate services on Kubernetes
, manage infrastructure with Terraform
, and build on AWS
. A core part of the role is designing platform capabilities that allow LLMs to safely and reliably interact with tools, data, and services via agent frameworks and MCP servers
.

• Design, build, and maintain high-performance backend services in Golang for ML and AI platform use cases.
• Develop REST and gRPC APIs for inference, processing pipelines, orchestration, and platform services.
• Implement asynchronous and distributed processing patterns (workers, queues, event‑driven systems).
• Ensure backend services meet production standards for scalability, reliability, and security.

• Build and operate backend systems supporting:
  • Video processing (frame extraction, metadata generation, embeddings, indexing).
  • Image processing (OCR, classification, detection, embedding generation).
  • Document processing (parsing, layout analysis, chunking, OCR, retrieval pipelines).
• Integrate ML inference services into backend workflows with attention to latency, throughput, and cost.
• Work closely with ML engineers and data scientists to product ionize models and pipelines.

• Build LLM‑enabled backend services using structured prompting, tool/function calling, and retrieval‑augmented generation (RAG).
• Design and implement agentic workflows (multi‑step reasoning, tool orchestration, retries, guardrails).
• Develop and operate MCP servers that expose internal platform capabilities (search, retrieval, processing, data access) to LLM‑based applications.
• Enforce security, access control, and observability for agent and MCP interactions.

• Design and maintain vector‑based…