Description review

Republic Services | Staff Engineer – Agentic AI | REMOTE (US) | Full-time | $175k is our salary midpoint + 20%

Republic Services · REMOTE (US) · back to the listing

HR standards

53/100

needs work

Title ↔ description

76/100

solid

Reads as

Machine Learning Engineer

96% confident

What this role officially is

data scientist — ESCO, the EU occupation classification

Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.

Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist

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What the listing never says

  • No section describes what the person would actually do. Scope clarity
  • No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency

The listing, marked up

target bonus

Republic Services is the second largest environmental services companies in North America — ~40K employees, thousands of trucks, hundreds of facilities, and a mountain of operational data that has never had an AI layer on top of it. We have a backlog of hundreds of Agentic AI ideas that we need to deliver and a foundation of ontologies, evaluations, guardrails, etc... that we need to build.
We're hiring a Staff Engineer now to be the first hire and plan to expand the team aggressively. You will own that platform end to end, not manage the people.
What you'll work on
- Agent orchestration: tool calling, planner/executor and multi-agent patterns, MCP/A2A integrations with enterprise systems
- Making enterprise context reliably available at inference time — retrieval, memory, hybrid search, reranking, access controls
- LLMOps that isn't hand-wavy: evals and golden datasets, prompt/agent versioning, tracing, token and cost budgets, CI/CD for agents
- Safety in a regulated enterprise: prompt injection, data exfiltration, PII handling, human-in-the-loop, audit logging
You should have
- 7– 10+ years1 building and scaling production systems, with recent, hands-on GenAI/agentic work in production (not just demos)
- Shipped agents or LLM pipelines at scale
- Solid data chops — vector stores, chunking, hybrid search, knowing when retrieval is the wrong answer
- Cloud experience (we're primarily AWS / Bedrock); comfortable with both containers and serverless
- Track record of technical leadership and mentoring without needing a manager title
Nice to have: LangGraph / Semantic Kernel / PydanticAI, GraphRAG, knowledge graphs, LangFuse/LangSmith, multimodal.
Comp & benefits: $175k is our midpoint for salary + 20% annual target bonus, 401(k) with company match, ESPP, medical/dental/vision, PTO. Remote eligible, prefer AZ timezones +/-1. Not sponsorship eligible.
Why you should want to work here: We build cool stuff
email me ([email protected]) directly with the most interesting agent you've shipped.

How this was produced

Highlights are found by rule, not by a model: each one is a phrase matched at a known position, and every note is a template we wrote. The two scores come from a typed-decision model (Jev) that reads the listing against the official role definition and real listings for the same role, and returns probabilities rather than prose — it never writes any of the words on this page, and never chooses what to highlight.

Deterministic penalty applied to the HR score: 20 points (from 73 before penalties). Reviewed 21 Sep 2026.