Samba TV tracks streaming and broadcast video across the world with our proprietary data and technology. We are on a mission to fundamentally transform the viewing experience for everyone. Our data enables media companies to connect with audiences for new shows and movies, and enables advertisers to engage viewers and measure reach across all their devices. We have an amazing story with a unique perspective on culture formed by a global footprint of data and AI-driven insights.

We’re seeking a motivated AI Automation Intern to join our AI Task Force (ATF) and help ship high-leverage automations across the company. You’ll work on n8n orchestration, LLM tool-calling, Model Context Protocol (MCP) servers, lightweight RAG/search, and observability/evals, all aimed at reducing toil and proving measurable ROI.

Tooling & Access (day one): You’ll be given access to the latest AI tools, including Claude Code, Cursor, and Claude.ai (plus other team-standard tools), with templates, examples, and mentorship to ramp quickly.

WHAT YOU'LL DO

• Workflow Orchestration (n8n): Build event-driven workflows (Slack/webhooks/cron/HTTP) and chain multiple LLM steps (router → tool calls → synthesizer; judge/fallback patterns).
• MCP Tools & Integrations: Extend MCP servers (search, ATS, audience/segments ops) with clear JSON schemas, validation, and error handling.
• Prompt Engineering (practical): Write robust system/task prompts with few-shot examples; enforce structured JSON outputs; manage cost/latency trade-offs.
• RAG & Search Hooks: Add retrieval (hybrid/BM25+vector) and citations for grounded answers; implement “no-answer” fallbacks.
• Observability & Evals: Log inputs/outputs, latency, tokens, cost, and success flags; create small golden-set evals and regression checks.
• Reliability & Ops: Add retries, backoff, dead-letter paths, Slack alerts; handle secrets, OAuth, pagination, and idempotency.
• Docs & Demos: Write concise READMEs/runbooks; present outcomes with metrics.

• Slack Triage Bot: Slack trigger → LLM classifier → MCP search → LLM synthesizer → Slack reply with confidence & citations; human-in-the-loop fallback.
• Candidate Finder (ATS): MCP tool for role queries; retrieve/rank candidates; weekly adoption & quality report.
• Audience Q&A ( API): Natural-language queries → segments lookup via MCP → RAG-backed answers with source links and size estimates.
• Cost & Quality Guardrails: Judge-LLM scoring with auto-retry under threshold; per-run cost caps and daily canary tests in n8n.
• Automation Templates: Turn successful patterns into reusable n8n templates with configuration knobs and built-in metrics.

• Production LLM Automation: How to ship reliable, observable LLM chains with tool-calling, schema enforcement, and fallbacks.
• Enterprise Integrations: Why MCP servers stabilize interfaces and speed adoption.
• RAG/Search in Practice: When to retrieve, how to cite, and when to say “no answer.”
• Impact Tracking: Tie automations to WAU, cycle-time saved, and $/seat ROI—and communicate results crisply.

• Pursuing a Bachelor’s, Master’s, or PhD in CS, Data/Information Systems, or related technical field.
• Expected graduation: [2026–2028].
• GPA 3.3+ preferred (strong projects/portfolio can substitute).
• Foundations: Comfort with at least one scripting language (JavaScript/TypeScript or Python), Git basics, and JSON/HTTP.
• APIs & Data: Willingness to learn auth, pagination, and simple SQL sanity checks.
• Quality & Logging: Basic assertions/testing mindset; interest in adding run-level logs/metrics.
• No prior expert experience required in prompt engineering or automation platforms (e.g., n8n)—you’ll get training, patterns, and mentorship.
• Automation: Exposure to n8n, Zapier, or Make.
• LLMs: Familiarity with structured prompting (JSON), tool/function calling, and context window basics.
• MCP & Tooling: Curiosity or experience with Model Context Protocol.
• RAG/Search: Embeddings, hybrid retrieval, reranking, and citation strategies.
• Observability: Langfuse (or similar) for traces, costs, and evals.
• Cloud & Apps: Basics of AWS/GCP, Slack APIs, and webhooks.
• Product Mindset: Ship small, instrumented wins quickly.
• Communication: Clear specs/acceptance criteria; concise status updates and READMEs.
• Collaboration: Comfortable pairing with PM/Eng/DS; receptive to feedback and reviews.
• Ownership: Proactive about fallbacks, edge cases, and not breaking prod.
• Learning Agility: Curious, iterative, and data-driven.
• Built at least one real automation (school, hackathon, or internship).
• Demonstrated two-LLM chaining (e.g., router → tool → synthesizer, or solver → judge).
• Hands-on with MCP-style tools, Slack bots, or lightweight RAG.
• Small eval/golden-set portfolio or open-source contributions.
• Experience showing outcomes (adoption, latency, cost) and presenting results.

Samba TV is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.  We strive to empower connection with one another, reflect the communities we serve, and tackle meaningful projects that make a real impact.

Samba TV may collect personal information directly from you, as a job applicant, Samba TV may also receive personal information from third parties, for example, in connection with a background, employment or reference check, in accordance with the applicable law. For further details, please see Samba's Applicant Privacy Policy. For residents of the EU , Samba Inc. is the data controller.