AI/ML Engineer

Application Deadline:
15 April 2026

Department:
Engineering

Location:
Portland, Oregon

Compensation:
$80,000 - $100,000 / year

Description
AI/ML Engineer
LLMs · Agentic Systems · Applied ML
*ClearlyRated · USA (Remote-West Coast Only) · Engineering*
We’re building AI that tells professional services firms which client relationships are at risk before the humans notice. Not demos. Not prototypes. Production AI on real enterprise data — and we’re just getting started.
About ClearlyRated
ClearlyRated is a B2B SaaS platform that helps professional services firms — from global engineering consultancies to staffing agencies — measure, understand, and act on client satisfaction data. Our NPS-driven platform processes millions of survey interactions, powers real-time relationship health scoring, and is in the middle of a significant platform evolution: new data integration architecture, event-driven survey automation, and a growing AI/ML capability stack built on Google Cloud.
We’re a small, focused engineering team building systems that operate at enterprise scale. That means the problems are real, the stakes are high, and every engineer on the team does work that matters.
Key Responsibilities
What You’ll Build
Our AI roadmap is live and shipping. You’ll work on systems that go from training and evaluation to production monitoring:
– Survey timing optimization model — an ML system that learns the optimal moment to send a survey for each client relationship, maximizing response rates and data quality
– NLP pipeline for free-text feedback analysis — classifying, scoring, and extracting structured signals from open-ended survey responses across thousands of enterprise clients
– Client health scoring — an aggregate model that combines survey results, response patterns, historical sentiment, and relationship signals into a single predictive score per account
– Agentic AI architecture using LLM orchestration (Google Vertex AI / ADK) — multi-agent systems that reason over client data and surface proactive recommendations to account managers
– RAG system over enterprise knowledge bases — grounding LLM outputs in verified client data and platform knowledge
– MLOps infrastructure: model versioning, A/B testing, inference cost monitoring, drift detection, and production observability for agent loops
Skills, Knowledge and Expertise
**Our Stack
Python**
Java
GCP Vertex AI
Google ADK
Kafka / Pub/Sub
MongoDB
Vector DBs
LLM APIs
MLOps
RAG
What We’re Looking For
–
ML fundamentals you can derive, not just apply.
You understand gradient descent, loss functions, regularization, and evaluation metrics at the level where you could implement them from scratch if you needed to.
–
Practical LLM experience.
You’ve built something real with LLM APIs — function calling, structured outputs, context management, prompt design under constraints. You know how they fail and how to build around that.
–
NLP intuition.
Tokenization, embeddings, semantic search, classification — you understand what’s happening inside the models you use.
–
Agentic architecture thinking.
You’ve thought about or built systems where AI agents plan, use tools, and hand off to each other. You understand the failure modes: loops, hallucinated tool calls, context overflow.
–
Production ML mindset.
You think about latency, cost, model drift, and monitoring before you think about accuracy metrics. A model that’s great in evaluation but unreliable in production is not a good model.
–
Python proficiency.
You write clean, testable Python. You know when to use a dataclass vs a dict and why it matters at scale.
Bonus Points
– Experience with Google Cloud AI stack: Vertex AI, Google ADK, Pub/Sub for agent communication
– Multi-agent coordination patterns: orchestrator–worker, queue-based handoffs, tool use with guardrails
– Fine-tuning experience — LoRA, PEFT, or full fine-tuning on domain-specific data
– Java experience — our backend is Java/Spring Boot, and ML systems that integrate deeply with the platform need engineers who can cross that boundary
– MCP (Model Context Protocol) integration experience
– Experience with vector databases: Pinecone, Weaviate, pgvector
Benefits
Why This Role Is Different
Most AI engineering jobs at this stage are either (a) prompt engineering wrapped in a Python script, or (b) infrastructure work with no meaningful ML. This role is neither. You’ll design learning systems, ship production models, and build agents that make decisions on behalf of enterprise clients. The data is real, the users are real, and the problems are genuinely unsolved.
We’re early enough that you’ll shape the architecture. We’re scaled enough that your work will be used immediately.
How We Hire
We hire on ability, not tenure. We don’t care whether your experience comes from a top university, a bootcamp, an open-source project, or a side hustle you built at 2am. What we care about is whether you can think clearly, build well, and learn fast.
Our interview process is deliberately hard. If you make it through, you’ll know you earned it — and so will we. We test fundamentals, systems thinking, and the ability to reason through problems you haven’t seen before. We don’t ask you to recite design patterns. We ask you to think.
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★
| Our AI/ML interview tests: ML fundamentals (you’ll derive things, not recite them), LLM system design, agentic architecture reasoning, and a practical exercise around a real problem from our domain. We care about how you think about failure modes, cost, and production reliability — not whether you can name every transformer variant. Strong Python and system design are expected. Java familiarity is a plus.