Vantaca's vision is big! We are the leading AI-native community management performance platform that enables owners and operators, community management teams, boards and associations to work smarter, faster, and with unprecedented insight. More than just accounting and management software, Vantaca is intelligent business operating software that leverages artificial intelligence to automate routine work, surface actionable insights, and help our customers increase revenue, efficiency, flexibility, and control. Built on modern cloud architecture with a single-platform design, Vantaca combines comprehensive functionality that adapts to 100% of business processes with AI-powered automation that learns and improves over time. Our proactive AI capabilities don't just report on what happened; they predict what's coming and recommend what to do next. From intelligent document processing and predictive analytics to automated workflows and conversational interfaces, we're transforming how community management companies operate. With seamless integrations across the software and banking ecosystem, we're building the intelligent hub for community management where AI doesn't just assist, it anticipates. Vantaca is focused exclusively on community management and is the trusted technology leader defining the AI-powered future of the community association management industry. We're building something fundamentally different, and our customers are experiencing the competitive advantage that comes from working with truly intelligent software. Vantaca just achieved unicorn status with a $1.25B valuation, so it's safe to say we're past the "scrappy startup phase." We're not just building a successful company, we're building the category-defining platform that will transform how an entire industry operates.
Overview
We are building a world-class Applied AI practice inside Vantaca's Applied AI team. We need someone who can ship production-grade ML and LLM systems for our Implementation and Client Enablement teams. This is not a prompt engineering role or an AI exploration sandbox. You will build systems that are evaluated, deployed, and observed — owning the gap between "interesting model" and "thing that reliably runs in production." You will partner with Implementation PMs, Solution Consultants, and Client Enablement Specialists to identify the highest-leverage problems and ship tooling that removes friction across the client lifecycle. The work is high-trust and high-autonomy — you own your problem space end to end.
What You'll Work On
Our Implementation and CE teams have a validated backlog of high-value AI builds — risk surfacing, workflow intelligence, client coaching, configuration assistance — and no dedicated engineering resources to execute on them. You change that.
Design and ship ML and LLM systems spanning supervised models that predict and rank, retrieval and generation systems that draft and summarize, and agentic workflows that act on internal data
Build evaluation infrastructure alongside every system — define success criteria before writing code, measure whether the system worked, and catch regressions before users do
Architect RAG, retrieval, and context engineering patterns that let LLMs operate reliably on internal knowledge and production data
Reason rigorously about modeling choices — label definition, leakage, time-aware splits, calibration, precision-at-k vs AUC, when a heuristic baseline beats a model
Work directly in Databricks and Unity Catalog — understand the operational data, write the SQL, and build systems that act on it
Own deployment and monitoring for everything you ship — feature drift, outcome tracking, LLM eval regression, retraining cadence, rollback paths
Treat data governance and access scoping as design constraints, not afterthoughts
Maintain versioned, traceable LLM workflows — prompts and context patterns that are reusable, not one-off
What We're Looking For
Production experience shipping both classical ML and LLM systems — strong opinions on when to use which
An eval-first mindset — you don't trust a system you haven't measured, and you build the measurement before the model
Fluency in a data warehouse environment — SQL, time-aware feature engineering, leakage discipline
Production scars — you've watched a model degrade in the wild, seen a label loop bias itself, caught an LLM provider regression with the prompt unchanged
Cost intuition — you can napkin-math the unit economics of an LLM workflow before committing to it
Ability to scope work in partnership with non-technical stakeholders, translating their pain into a buildable system
Comfort with distinguishing the business metric from the model metric, and arguing for the right one
Requirements
Technical Requirements
You use AI tools (Claude, Cursor, Claude Code, or equivalent) as a core part of your daily workflow. Not occasionally. As a thinking partner and execution accelerator.
Python proficiency as your primary build language for automation and scripting
Full-stack range: comfortable building APIs, automations, integrations, and lightweight UIs without needing a separate front-end resource
SQL and data fluency: you will work regularly in our data warehouse and need to understand and act on operational data directly
API integration experience: REST, webhooks, OAuth
RAG and retrieval system experience: chunking, embedding strategies, retrieval quality, hallucination mitigation
Prompt and context engineering: you understand why context boundaries matter and have a strategy for what to persist vs. retrieve
DevOps fundamentals: CI/CD, Infrastructure as Code, containerization. You ship and maintain what you build.
AI/ML background is required; CS or AI/ML academic track preferred
Mindset and Approach
Spec-first by default: you write detailed intent documents before building. Resistance to structured planning upfront is a disqualifier for this role.
Bias toward shipping: you prefer a v1 in two weeks over architecting a v3 for two months
Product sense for non-technical users: you can translate operational pain into a scoped technical solution without requiring a detailed spec from the person who has the problem
Comfortable as the first and only: you are energized by operating as the sole AI engineer in a domain, without a peer engineering org to lean on day to day
Builder, not buyer: you build internal tooling and do not stitch together SaaS products
Security-aware: you ask about access scoping and data classification before you build, not after
Comfortable with ambiguity: requirements will shift; you orient toward the outcome
Strong written communicator: you document your intent before you build and leave clear records of what you built and why
Core Values
Always Growing:
Likes change and enjoys finding new ways to improve their knowledge and the product. Always ready to learn quickly, helping themselves and the team grow.
Win as a Team:
Builds trust and works together by making sure everyone communicates well. Actively involved in daily work, working closely with the team, listening to their ideas, and celebrating successes together.
Accountability Starts with Me:
Notices problems and takes personal action to solve them.
Unwavering Commitment to Customer Experience:
Regularly talks to residents and management companies, taking personal responsibility to understand what they need, address concerns, and make their experience better with improved Vantaca processes.
Innovate Boldly:
We challenge the status quo and push boundaries to create meaningful change. We act with urgency and purpose, knowing that innovation drives our success.
Why You Should Join Our Team
Build consumer products that millions use.
Shape how homeowners across the country interact with their communities every day.
AI-First Product Culture
with access to cutting-edge tools and autonomy to experiment.
Our eNPS is +68! (Google it, that is great)
Benefits: Medical, Dental, and Vision kick in day one
Unlimited PTO (with a requirement for employees to take a minimum of one continuous week per year)
401K with Company Match
Remote Flexible - come to the office when needed
Great parental leave benefits
Unicorn-stage growth: