Peppercorn Solutions is building the infrastructure layer that wealth management has been missing. RIAs and wealth managers are allocating more to alternatives than ever — interval funds, BDCs, evergreen structures — but the tools to actually see, manage, and optimize those positions alongside Peppercorn Solutions is building the infrastructure layer that wealth management has been missing. RIAs and wealth managers are allocating more to alternatives than ever — interval funds, BDCs, evergreen structures — but the tools to actually see, manage, and optimize those positions alongside public securities don’t exist yet. We’re fixing that.

Our platform is an AI-native unified portfolio engine that brings public and private investments into a single, deterministic view. Not a dashboard, not a chat interface — a computation engine that handles the math that other tools hand-wave away. When an advisor needs to understand liquidity exposure across a client’s full book, or run a rebalance that accounts for redemption windows and tax lots at the same time, that’s what Peppercorn is built for.

We’re pre-seed, Boston-based, and moving fast. The engineering problems here are genuinely hard — data normalization across inconsistent fund structures and feeding quality data to deterministic financial computations and AI systems at scale. This role reports directly to the CTO and is designed for someone early in their career who learns fast, asks sharp questions, and wants to grow into a senior engineer by building foundational infrastructure from scratch.

Work Status

Visa Sponsorship: Candidates must be authorized to work in the U.S. without current or future sponsorship. We welcome candidates on F-1 OPT/STEM OPT.

Location: Remote. Boston-area preferred; occasional in-person collaboration.

Compensation: This is a ground-floor role at a pre-seed company. Starting compensation reflects our stage; as we raise, comp scales with the company, with opportunity for equity participation.

What You’ll Do

• Your primary domain is the data layer, but you won’t be siloed there. You’ll work across the Python stack — services, APIs, pipelines, and tooling — and you’ll be expected to pick up new technologies quickly. You don’t need to know everything on day one; you need to be capable of learning it and holding your own in a technical discussion about it.

• Build and evolve Python services and APIs (primarily FastAPI) that power the platform — from designing endpoints to connecting the data layer to front-end and external consumers.

• Design and build data models, schemas, and database infrastructure, learning how financial data needs to be structured for correctness and performance.

• Write clean, performant SQL and build data pipelines — from ingestion through ETL/ELT workflows — to move financial data reliably from custodians, market-data providers, and third-party services through to analytics.

• Work with engineers and product stakeholders to translate business requirements into scalable database designs, learning to optimize stored procedures, views, indexes, and query plans along the way.

• Write well-tested Python code — you’ll learn pytest, CI pipelines, and type-safe patterns as part of daily work.

• Participate in code reviews, design discussions, and continuous improvement of our data architecture and engineering practices.

• Contribute to data quality, observability, and governance standards as you and the platform grow together.

What You Need

• 0–3 years of relevant professional experience. Recent graduates with strong academic projects, internships, or personal projects are encouraged to apply.

• Solid foundation in software engineering fundamentals: data structures, algorithms, version control (Git), and testing.

• Comfortable with SQL — you can write queries, understand joins and indexes, and are eager to go deeper into execution plans and schema design.

• Experience with PostgreSQL or a comparable RDBMS (MySQL, SQL Server).

• Working proficiency in Python and a willingness to write production Python daily. Some exposure to a Python web framework — we use FastAPI, but Django or Flask experience translates well. Comfort with testing (pytest) and modern Python patterns is a plus.

• Foundational understanding of data modeling concepts — normalization, indexing, referential integrity — or a clear ability and motivation to learn them quickly.

• A genuine enthusiasm for working with data – you enjoy thinking about how information flows, is stored, and is queried.

• Ability to leverage AI tools and platforms to accelerate development.

• Comfort with ambiguity — you'll help define problems, not just solve pre-scoped tickets.

• Ability to engage in strong technical back-and-forth — you form opinions, defend them with reasoning, and update them when presented with better arguments.

What’s Good to Have

• Experience with data pipeline or orchestration tools (dbt, Airflow, Dagster, or similar).

• Exposure to cloud data services (AWS RDS/Redshift, GCP BigQuery/Cloud SQL, or Azure SQL).

• Familiarity with Docker, CI/CD pipelines, and infrastructure-as-code concepts.

• Interest in or experience with data visualization and reporting (charts, dashboards, financial reports).

• Coursework or project experience in financial data, accounting, or quantitative analysis is a strong plus.

• Exposure to React or modern front-end frameworks – enough to collaborate effectively across the stack.

Why Peppercorn

You’ll report directly to the CTO and work alongside a small team, with senior leadership that takes mentorship seriously. This isn’t a role where you’ll be handed tickets and left alone — you’ll pair on architecture decisions, get real code review, and be expected to contribute to technical discussions from day one. The goal is for you to grow into a senior engineer here, not to stay junior.

Within your first few weeks you’ll be shipping code, reviewing schemas, and contributing to architectural discussions. The ramp is steep, but the support is real.

The codebase is early, which means you’ll shape it rather than inherit it. You’ll work on problems that are genuinely hard and you’ll have the ownership and support to solve them well. Because we’re building for financial advisors who depend on accuracy, the bar for correctness is high, which makes the work worth doing.