Lead Software Engineer

Engineering · Hybrid, Boston, Massachusetts, United States (3 days on-site) · Occasional travel a few times per year

You will take ambiguous work after product-market fit and turn it into systems other engineers can extend. In your first year you ship unclear projects, raise the engineering bar through review and fundamentals, and make ML work measurable without trading away reliability. You also decide what the data pipeline should keep versus replace and leave a cleaner foundation behind.

• Deliver ambiguous projects in the first year while raising code quality, review standards, and ownership of fast iteration.
• In the first six months, make a keep-versus-replace call on the data pipeline and execute toward a solid, extendable foundation.
• By 90 days, know pipeline bugs well enough to clean them and have a foundation plan others can follow.
• By 90 days, get junior engineers working on small tasks that help; keep weekly mentoring and PR coaching going.
• Gather requirements yourself, implement high-impact systems, and write design docs that treat cost, scale, and maintainability as first-class.
• Surface team-level problems, get buy-in on tests and simplicity, and align test strategy across teams.

• Design and implement production Python systems others can extend.
• Map the existing pipeline, fix bugs, and execute incremental cleanup without reckless rewrites.
• Work with orchestration and data stores (Dagster or Airflow, S3, warehouse, OpenSearch) as a practitioner; consume Kubernetes rather than administer it.
• Choose ML metrics, validate changes, and improve models while holding reliability—including when LLMs are in play.
• Write technical design docs, flag material AWS-bill impact, and own the outcome.
• Review PRs, coach juniors, and explain the next project so they can contribute real work.
• Dig into what internal customers and the live system actually do before you redesign.

Required

• Expert production Python: you have owned complex systems end-to-end and can talk about how others extended them.
• Expert pipeline judgment: you can map a messy path, decide keep versus throw away, and build a cost-aware foundation.
• You grow juniors through issues, PR review, and small tasks that actually help—without being a people manager.
• Practical pre-LLM ML evaluation: you pick metrics, validate, and improve systems without sacrificing reliability.
• You write design docs others implement against, including cost, scalability, and long-term maintainability.
• You raise the bar in review, tests, and simplicity beyond your own tickets.
• You can hybrid in Boston three days per week.

Nice to have

• Cross-team influence on test strategy and practices without formal authority.
• Cost-aware AWS judgment: you flag wild bill impact and still spend when it is the right call.
• SQL (MySQL or Postgres) to debug pipelines and answer product questions.
• Exposure to fraud or cybersecurity investigations workflows—not required.

Base salary $190,000–$230,000 USD. Bonus 15. Equity: A round Equity.