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.)
Job Summary
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 toolchains 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.
Job Description
Backend Engineering (Golang)
- 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.
**ML Platform & Processing Pipelines **
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 productionize models and pipelines.
LLMs, Agents, and MCP Servers
- 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.
**Vector Search & Retrieval **
- Design and maintain vector-based retrieval systems using Milvus.
- Implement embedding ingestion, indexing, and query pipelines at scale.
- Optimize retrieval quality, latency, and relevance for downstream LLM applications.
**Cloud, Kubernetes & Infrastructure **
Deploy and operate backend and ML services on Kubernetes (scaling, rollouts, resource management).
Use Terraform for infrastructure provisioning and continuous delivery of cloud resources.
Build and operate primarily on AWS, leveraging services such as:
- Compute, networking, and IAM
- Object storage
- Managed Kubernetes
- Logging and monitoring services
Reliability, Quality & Operations
- Implement observability using logs, metrics, and traces; define SLOs and alerts.
- Write automated tests (unit, integration) and contribute to CI/CD pipelines.
- Participate in on-call rotations and incident response; drive post-incident improvements.
**Required Qualifications **
- 3–6 years of professional software engineering experience.
- Strong backend engineering experience with Golang.
- Experience building and operating APIs (REST and/or gRPC) in production.
- Hands-on experience with Kubernetes in production environments.
- Experience using Terraform for infrastructure provisioning and deployment.
- Solid working knowledge of AWS cloud services and core architectural concepts.
- Experience building or supporting ML processing pipelines (video, image, or document).
- Practical experience using LLMs in production systems.
- Experience developing agents and/or MCP servers, or equivalent tool-integration platforms.
**Preferred / Nice-to-Have Qualifications **
- Experience with Milvus or other vector databases in production.
- Familiarity with GPU-backed workloads and ML inference optimization.
- Experience with messaging/streaming systems (Kafka, SQS, SNS, etc.).
- Knowledge of secure system design for AI platforms (IAM, secrets management, least-privilege access).
- Experience working on internal developer platforms or ML infrastructure teams.
Disclaimer:
- This information has been designed to indicate the general nature and level of work performed by employees in this role. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications.
Skills
Cloud Platform, Collaboration, Go Programming Language, Kubernetes, Large Language Models (LLMs), Model Context Protocol
Compensation
Primary Location Pay Range: $142,651.46 - $213,977.19
Comcast intends to offer the selected candidate base pay within this range, dependent on job-related, non-discriminatory factors such as experience. The application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later.
Base pay is one part of the Total Rewards that Comcast provides to compensate and recognize employees for their work. Most sales positions are eligible for a Commission under the terms of an applicable plan, while most non-sales positions are eligible for a Bonus. Additionally, Comcast provides best-in-class Benefits to eligible employees. We believe that benefits should connect you to the support you need when it matters most, and should help you care for those who matter most. That’s why we provide an array of options, expert guidance and always-on tools, that are personalized to meet the needs of your reality – to help support you physically, financially and emotionally through the big milestones and in your everyday life. Please visit the compensation and benefits summary on our careers site for more details.
Education
Bachelor's Degree
While possessing the stated degree is preferred, Comcast also may consider applicants who hold some combination of coursework and experience, or who have extensive related professional experience.
Certifications (if applicable)
Relevant Work Experience
5-7 Years
Comcast is an equal opportunity workplace. We will consider all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, genetic information, or any other basis protected by applicable law.