Staff Software Engineer

About the role

Engineers on the AI Developer Foundations will build the tools that build the product. It's a new team that owns how AI gets used inside engineering, product, and design at SimplePractice: the conventions for using these tools, the context agents read before they touch our code, the bar generated work has to clear before a human spends time on it, and the training that makes AI a useful tool to all of engineering. This is a hands-on role reporting to the VP of Technology. You'll write code most days, mostly in and around a large Rails monolith, and you'll also write the conventions other engineers follow.

Responsibilities

• Own the context layer agents read: rules files, docs conventions, grounding data, with CI and named owners keeping it current
• Publish the standards for how we prompt coding agents, how we supply context, and what a spec needs to contain before it's worth handing to an agent
• Define the review gates any AI-generated artifact clears before a human spends time on it, and get what reviewers catch flowing back into the conventions
• Take the prototypes running today and turn the ones that hold up into supported workflows that run for someone other than their author
• Evaluate harnesses, models and vendors against criteria and eval sets
• Work the product-to-engineering handoff with PMs, designers and EMs
• Teach. Documentation, worked examples, office hours, and time sitting with a squad while they work with the tools we build
• Help us draw the boundaries for where an agent runs unattended, where a human signs off, and where we don't use one at all

Requirements

• BS/MS in Engineering, Computer Science, or related field, or equivalent experience
• 7+ years building production software and then maintaining it. You’ve shipped something, lived with the decisions, and found out whether a design decision held up
• Strong problem-solving and communication skills; comfortable in fast-paced, cross-functional environments
• Ruby on Rails
• Recent, direct Rails experience in a large codebase.
• Ability to read unfamiliar application code quickly and judge whether what an agent produced in it is any good

Skills

• AI-Assisted Engineering
  • LLM-backed systems you've shipped to users and kept running, past the point where better prompting stops helping
  • Experience building context or memory handling, and writing evals
  • Judgment about what belongs in code and what belongs in the model
  • Track record evaluating tools with evidence
• Developer Tooling & Enablement
  • Internal tools, platforms, conventions or workflows that other engineers chose to use.
  • CLI and harness-shaped software, and comfort connecting systems over APIs
  • Strong writing. Much of this role is getting other people to work differently, which happens through docs and worked examples rather than announcements
  • Comfort working outside engineering, explaining technical constraints to people who don't share your background and taking their pushback seriously
• Bonus Points
  • Agent harnesses, MCP servers, orchestration frameworks
  • Healthcare or another HIPAA-regulated environment
  • AWS, Terraform, Kubernetes, Docker
  • Experience on a platform or infrastructure team where the customers were other engineers
  • Having overbuilt a platform once before the workflows existed, and learning to spot the warning signs earlier

Benefits

• Medical, dental, vision, life & disability insurance
• 401(k) plan with company match
• Flexible Time Off (FTO), wellbeing days, paid holidays, and summer Fridays
• Mental health resources
• Paid parental leave & Backup Care
• Tuition reimbursement
• Employee Resource Groups (ERGs)

Pay

Base Compensation Range: $249,120 USD - $311,400 USD

Base salary is one component of total compensation. Employees may also be eligible for an annual bonus or commission. Some roles may also be eligible for overtime pay.