Advisors use Helmsted for financial planning, tax and estate analysis, meetings and their client portal, and our goal is to replace a firm’s entire tech stack. We are a small team, live with pilot firms and shipping every day.
Both roles are founding hires. They report to the co-founders, start remote anywhere in the US, and come with founding equity.
Founding AI Engineer
Build the agents and intelligence behind Helmsted, the AI-native operating system for wealth advisors of complex households.
View PDFThe role
You will work directly with our CTO on the core of the product: the agents that do the work and the intelligence behind them. You will experiment constantly with what the frontier labs release and turn the best of it into a Helmsted that reasons, plans and executes better, faster and more efficiently. The goal is an intelligence layer that lets advisors focus on clients, not the fragmented, chaotic tech stack they have lived with for thirty years.
What you’ll do
- Make it low-lift for advisors to keep consolidating their stack into Helmsted (integrations, browser control, agents that finish real tasks), our top priority as work moves away from typing and clicking
- Build features end to end, from agent logic and tools to what advisors and their clients see
- Improve how Helmsted reasons through a household’s planning, tax and estate questions, and the reports it writes
- Own quality with evals, guardrails and source tracing, so every output is accurate and ready for advisor review
- Own AI security: least-privilege agents, permission-aware retrieval and memory, and defenses against prompt injection and data leakage that you red-team yourself
- Make the model layer efficient: route each task to the cheapest model that does it well, and tune caching, batching, memory, retrieval and context for cost and latency
- Run a constant experiment loop: put promising new models and techniques through our evals within days of release, and ship what makes Helmsted better, faster or cheaper
- Work directly with advisors at our pilot firms and turn what you learn into product
What you bring
- A record of shipping LLM-powered products that real people use: agents, tool use, retrieval, evals
- Deep understanding of how AI systems leak and get attacked: prompt injection through emails, documents and web pages; data leaking between clients through retrieval, memory or caching; tool misuse; and which client data should ever reach a model provider
- Strong Python and TypeScript, and comfort across the stack (APIs, Postgres, React)
- Deep hands-on time with frontier model APIs, and judgment about which model fits which job
- An experimenter’s discipline and an efficiency mindset: fast, measured trials on new models, and a record of cutting cost and latency without losing quality
- High agency: you find the problem, decide with imperfect information and ship
- Pro-AI: you can build through AI tools almost exclusively, and still ship without them when you have to
Highly preferred
- Fintech or wealthtech experience (advisor software, custodians, banking, payments)
- Working knowledge of the rules advisors operate under (SEC and FINRA oversight, fiduciary duty, Reg S-P privacy, books and records)
- Browser automation or computer-use agents
- Fine-tuning or serving open-weight models
- AI red-teaming or security work (OWASP Top 10 for LLM and Agentic Applications), or building inside a SOC 2 program
- Fast decision models (models that return a choice with a calibrated probability instead of text) used to route, triage or check agent actions
Why join
- Founding equity and real ownership of the product, the stack and how we build
- Start remote anywhere in the US
- Freedom to experiment, with every frontier model and AI tool you need covered
- Work directly with the founders and our pilot firms, and see advisors use what you ship
Pay and details
Final offers are contingent on a background check.
Skip the cover letter. Email careers@helmsted.ai with short answers to these; bullets are fine. We want to see how deep your thinking on AI systems goes. Use AI to help you answer, and tell us how you did.
- 01Design a system that measurably improves every week from its own usage, without retraining a base model. What does it learn from, how do you stop it learning the wrong lessons, and how do you prove it got better?
- 02An agent works with the same people for years. How would you design its memory so it learns what matters and forgets what it should?
- 03One shared “brain” serves a whole organization, but each person, team and client may only see and do what they are permitted to. How would you keep that brain divisible and permissioned across retrieval, memory and agent actions, and prove nothing ever crossed a line?
- 04Your agents must reason over millions of documents, records and conversations that change daily. What goes into context, what gets retrieved, summarized or fine-tuned, and how would you know when retrieval is quietly failing?
- 05Every task could run on the best frontier model, but cost, latency and rate limits say otherwise. How would you route work across frontier and open models, caching and batch jobs, and how fast could you prove whether a new release should change that?
- 06A client sends their advisor a PDF with hidden instructions telling the agent to email account statements to an outside address. Walk us through every layer that stops it, and how you would prove those layers keep working as models change.
Add a resume, GitHub or LinkedIn, plus a link to an agent or AI system you’ve shipped if you can. We move fast.
Email careers@helmsted.aiFounding Investment Data Engineer
Own the investment data behind Helmsted, the AI-native operating system for wealth advisors of complex households.
View PDFThe role
You will own how investment data comes into Helmsted, how it is protected, and everything built on it, from daily custodial files to performance reporting, fee billing and, in time, trading. You will work with our CTO on architecture and with our CEO, a CFA charterholder and CFP® professional, on methodology.
What you’ll do
- Own the investment data foundation, so account data arrives clean and reconciled every day
- Protect client PII at every step: secure file transfer from custodians, encryption, least-privilege access, and masking account numbers and tax IDs anywhere they aren’t needed, including what reaches an AI model
- Build the checks that catch bad data before an advisor sees it
- Build performance reporting that advisors can defend to clients and examiners
- Build fee billing that is right to the dollar, every cycle
- Grow our data connections to custodians and other providers, and lay the groundwork for trading and rebalancing
- Bring in market and research data (prices, corporate actions, filings, news) so Helmsted’s AI can analyze investments and cite its sources
- Keep the data clean and traceable to source, so Helmsted’s AI can reason over it and cite it
- Put new models to work on data tasks (extraction, reconciliation, exception handling) as they ship, and keep the cost of running AI over every account, every day, under control
What you bring
- 5+ years in data engineering, with real depth in investment data and strong Python and SQL
- Investment data files: how custodial files actually behave (positions, transactions, securities, cost basis), including late, missing and restated files, and how they tie out
- Performance nuances: TWR vs. MWR, gross vs. net of fees, cash-flow timing, trade-date accounting and accrued income, why two systems can disagree on one account, and the standards behind it all (GIPS, the SEC Marketing Rule)
- Fee billing and its rules: tiered and household schedules, breakpoints, exclusions, advance vs. arrears, proration and custodian fee deduction, plus keeping fees matched to the advisory agreement and Form ADV, where SEC examiners often find errors
- Precision with money: decimal math, idempotent pipelines, audit trails and a reconciliation mindset
- Fintech security instincts: you know which data is sensitive (account numbers, tax IDs, balances, holdings), how it leaks (logs, exports, test data, AI prompts), and how to minimize, mask and audit it
- Pro-AI: you can work through AI tools almost exclusively, and still get it done without them when you have to
Highly preferred
- Built performance, billing or trading systems at a portfolio accounting, reporting or wealthtech company
- Operations experience at an RIA, family office, broker-dealer or custodian
- Experience with trading systems (rebalancing, order management, trade files) or account aggregation data
- Handling sensitive client data under SOC 2 and the SEC’s Reg S-P, including the 72-hour breach notice advisors must require from vendors like us
- Experience with market and reference data vendors, including their licensing and redistribution rules
Why join
- Founding equity and real ownership of the data foundation everything else runs on
- Start remote anywhere in the US
- A modern stack and no legacy systems to work around
- Work directly with the founders and our pilot firms, with every AI tool you need covered
Pay and details
Final offers are contingent on a background check.
Skip the cover letter. Email careers@helmsted.ai with short answers to these; bullets are fine. We want to see how you think AI will change this work and how you would build for it, securely. Use AI to help you answer, and tell us how you did.
- 01How do you work today? Walk us through the AI tools and agents in your data work, what you trust them with and what you still check by hand.
- 02Reconciliation, performance and billing are still run largely by people chasing exceptions in spreadsheets. How will AI change that work over the next five years, and what would you build first?
- 03Pick one: a custodian restates a week of transactions, two systems show different quarterly returns for the same account, or a mid-quarter deposit crosses a household billing breakpoint. How would an agent catch it, resolve it and show its work?
- 04Which parts of this work would you let an agent run end to end, and where would you keep a person in the loop? How would you secure client data and keep an audit trail either way?
- 05A nightly custodial file lands with account numbers, tax IDs and balances for every client. Walk us through how it gets from the custodian to an AI-written client report without exposing PII to anyone, or any model, that doesn’t need it.
Add a resume or LinkedIn. We move fast.
Email careers@helmsted.aiHelmsted, Inc. is an equal opportunity employer. We evaluate candidates on merit and do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, gender identity, age, veteran status, or disability.