Dealpath is modernizing its approach to software quality. We're moving from a centralized manual QA team to a model where delivery teams own quality end-to-end, supported by embedded Quality Engineers. We need a Principal-level technical leader to drive that transformation as a hands-on individual contributor.
This is not a people-management role. It's a high-impact senior IC role where your leverage comes from technical depth, judgment, and influence — building shared tooling, setting the bar, and being the person every QE and engineer goes to with hard quality questions.
You will build the frameworks, set the standards, coach the embedded QEs, and own the quality infrastructure for our agentic product direction.
This is a full-time role, with a hybrid schedule that includes three days a week in the office in San Francisco.
You’re excited about this opportunity because you’ll:
Set the technical direction for quality across Dealpath's engineering organization — defining the test pyramid strategy, coverage thresholds, release criteria, and tooling choices that shape how every delivery team ships.
Build and own Dealpath's AI evals practice, creating the infrastructure, metrics, and regression frameworks that ensure our agentic product features meet a rigorous quality bar.
Champion AI-forward quality engineering by using AI tools consistently in your own work and coaching the QE community to do the same: writing better test code, reviewing AI-generated output critically, and applying AI to accelerate evaluation workflows.
Drive the production-quality feedback loop — owning blameless postmortems, RCA practices, and the org-wide quality scorecard that makes escape rates and systemic issues visible to engineering leadership.
Coach and grow embedded Quality Engineers across delivery teams, running the QE community of practice and setting the hiring bar for every QE role.
Influence without authority — driving practice adoption across multiple teams through technical credibility, shared tooling, and principled frameworks that become the standard.
Collaborate closely with VP Engineering and Engineering Managers to evolve Dealpath's quality culture, infrastructure, and long-term engineering strategy.
We’re excited that you are:
Bringing deep technical expertise with 7+ years in Quality Engineering or SDET roles, with significant hands-on automation experience in modern SaaS environments.
A hands-on coder — you write automation frameworks, build shared libraries, and pair with engineers on test design every day.
Experienced in driving quality practice across multiple teams without direct authority, with impact that comes from technical credibility and influence.
Skilled in building evals for LLM-based or agentic features, with familiarity in at least one eval framework (LangSmith, Braintrust, DeepEval, or equivalent).
Strong in modern automation frameworks (Playwright, Cypress, or similar) and at least one programming language used in modern SaaS engineering (TypeScript, Python, Go).
Experienced with blameless postmortems and structured RCA practices (5 Whys, Fishbone, CAPA, or equivalent).
Comfortable with observability and product analytics platforms (Sentry, Datadog, Pendo, Amplitude) and using them to drive testing strategy.
An excellent communicator, able to work cross-functionally with Engineering, Product, Design, and Customer Success.
Excited to work in a fast-paced, growth-stage startup environment where your decisions shape the entire engineering culture.
Fluent in AI-assisted development. You use tools like Cursor, GitHub Copilot, or similar regularly in your own work, apply a deliberate review process before relying on AI output, and are ready to help engineers and QEs on your team develop the same habits.
Qualifications:
7+ years in Quality Engineering, SDET, or equivalent software engineering roles with a quality focus.
BS in Computer Science, Software Engineering, or equivalent experience.
Proven track record driving quality transformation in a startup or growth-stage SaaS environment.
Hands-on experience building evals for LLM-based or agentic features.
Deep proficiency in at least one modern automation framework (Playwright, Cypress, or similar).
Track record of delivering quality programs for enterprise SaaS products, ideally in B2B, FinTech, or PropTech.
Strong organizational skills with the ability to balance technical depth and systemic quality improvement.
Consistent, hands-on experience with AI coding tools (Cursor, GitHub Copilot, Claude, or similar). Uses AI regularly, validates output before shipping, and can coach engineers to build the same disciplined habits.
Bonus Experience (Not Required) Build and Own the AI Evals Practice:
As Dealpath moves toward agentic features, prior experience standing up an AI eval practice is a strong plus — this is the kind of work we mean:
Build eval infrastructure: datasets, automated graders (deterministic checks + LLM-as-judge), and regression suites running in CI.
Define quality metrics for each AI feature — task completion rate, tool-call accuracy, hallucination rate, response quality, latency, and cost-per-task.
Maintain the eval dataset as production code: every production AI failure becomes a regression test, building a flywheel that prevents repeat escapes.
Run both offline and online evals — curated datasets during development, plus sampled production traffic for drift detection.
Select and own eval tooling: LangSmith, Braintrust, DeepEval, or equivalent.
Nice to Have
Experience at an AI-era startup or AI-native company (Vercel, Linear, Anthropic, or similar)
Real estate, fintech, or enterprise SaaS domain experience
Experience contributing to a manual-to-automation transformation
Performance, security, or chaos engineering background
Conference talks, open-source contributions, or other public technical leadership signals
The Perks & Culture:
Medical, dental, & vision insurance coverage
Hybrid 3-days in office policy in San Francisco
Flexible Spending Account
Paid Parental Leave
401(k)
Company-sponsored commuter benefits
Flexible time off policy
Catered lunches and snacks
Monthly wellness reimbursement
The estimated pay range for San Francisco candidates for this position is $ 180,000– $230,000 base.
This position is also eligible for Dealpath’s equity plan. Your compensation will be based upon several factors including your experience, qualifications, education, location, and the skills assessed in Dealpath’s interview process.
Your actual compensation will be confirmed in writing at the time of offer.
Dealpath’s compensation ranges are determined by current market data, so compensation data posted on our job posts may change as new market data becomes available.
About Dealpath
Dealpath is the world’s largest and most trusted AI-powered operating system for real estate investing. To date, the platform has powered more than $10 trillion in transactions, partnering with hundreds of firms, including leading global institutions such as Blackstone, Nuveen, LaSalle, CBRE IM, MetLife, Newmark, New York Life, UBS, Manulife, DWS, and Principal.
By bringing together the only private exchange for institutional real estate listings, investment data, and intelligent execution workflows into a single platform, Dealpath enables firms to capture more opportunities, make faster decisions, execute efficiently, and deliver superior returns.
Our company is led by an experienced team and backed by a combination of top tier venture capital firms and strategic industry partners including: Blackstone, Nasdaq, 8VC, JLL Spark, WTI, GreenSoil Investments, LeFrak, Milstein, Bechtel, and Morgan Stanley Expansion Capital.
We value your voice! If you get excited about solving real business challenges and working closely with other smart folks in a winning culture — we’d love to meet you!
Interview Process Notice
Dealpath conducts interviews via video conference. To support our interviewers with accurate note-taking, we may use AI-assisted transcription tools during your interview sessions. By participating in the interview process, you acknowledge that interviews may be recorded or transcribed using these tools for note-taking purposes only. Transcripts are used solely for internal evaluation and are not shared outside of the hiring team.