AI/ML Engineer at ClearlyRated

Portland, Oregon, United States -

Full Time

Start Date

Immediate

Expiry Date

14 Jun, 26

Salary

100000.0

Posted On

16 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Java, GCP, Vertex Ai, Google Adk, Kafka, Pub/Sub, Mongodb, Vector Dbs, Llm Apis, Mlops, Rag, Llm Orchestration, Prompt Design, Semantic Search, System Design

Industry

technology;Information and Internet

Description

AI/ML Engineer Application Deadline: 15 April 2026 Department: Engineering Employment Type: Full Time 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. | ★ | 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.

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Responsibilities

The engineer will build and deploy AI systems ranging from training and evaluation to production monitoring, focusing on tasks like survey timing optimization, NLP for feedback analysis, and client health scoring models. Key projects also involve developing agentic AI architectures using LLM orchestration and implementing RAG systems over enterprise knowledge bases.