Senior Software Engineer
Employees are confused about their benefits, and the leaders funding that second-largest expense often can't tell what's working. Avante is changing that: We're an AI-native benefits intelligence platform already trusted by brands like Samsung, News Corp, and Zscaler, with two purpose-built agents. Carly gives employees personalized, always-on guidance, and Ava gives benefits leaders analyst-grade answers on cost, utilization, and outcomes in seconds, closing the loop between employee experience and strategy.
The Role
As a Senior Software Engineer at Avante, you will own outcomes end-to-end: writing production code yourself where it matters most, and directing AI agents to handle implementation elsewhere, with full accountability for what ships either way. This is a hands-on, high-ownership role. We are looking for an engineer who solves problems pragmatically, whether that means building directly or steering an agent to do it well, and who brings the judgment and maturity to guide both human teammates and the AI systems increasingly doing implementation work alongside them.
What You'll Do
AI-Native Engineering
- Direct AI agents to handle implementation where appropriate, remaining accountable for what ships either way
- Push toward a "touchless PR" workflow where your judgment, not your keystrokes, is the bottleneck
- Use AI coding tools (Claude Code, Cursor, or similar) as a core part of how you build, with sharp instincts for where they help and where they don't
- Help the team decide how and when AI agents get used, and mentor teammates on working effectively alongside them
Product & Customer Impact
- Collaborate closely with product, design, and engineering peers to deliver solutions that drive real business value
- Mentor teammates by sharing context, giving feedback, and helping them grow their craft
Ownership & Execution
- Own outcomes across our stack, writing production code directly where it counts
- Design and build scalable services, APIs, and user-facing features within our TypeScript monorepo
- Integrate with external systems such as HRIS and ticketing platforms
- Model data, design schemas, and optimize queries to keep our systems fast and reliable
- Build for production with a focus on observability, monitoring, and testability from day one
- Participate in design discussions and code reviews
What We're Looking For
- Deep expertise in backend engineering, with solid working knowledge of the other side
- Proven track record of delivering complex features end-to-end in production systems
- Deep experience with TypeScript or other modern application development languages such as Java, C#, or Python
- Familiarity with system architecture, API design, and scalable engineering patterns
- Proficiency with databases (schema design, query optimization, performance tuning)
- Active, hands-on engagement with AI engineering, including real experience with LLM APIs, RAG, agent workflows, evals, or tool-calling, and a clear point of view on how AI has changed what you own and how you work. This goes beyond openness to using AI tools
- Startup or high-ownership experience: comfortable operating without a large team or established process behind you
- Strong product and customer judgment: a track record of trade-off calls that account for what's actually right for the end user, not just what's technically correct
- Ability to operate effectively in ambiguity, prioritize well, and balance speed with maintainability
- Strong communication skills and empathy for both teammates and end users
- Initial screen with our recruiters
- Video Interview with our Engineering Manager
- Technical interview focused on real-world problem solving (no whiteboard algorithms)
- On-site interview loop:
- Team behavioral interview (60 min)
- Session with our Engineering Manager (60 min)
- Final conversation with our CTO (30 min)
How We Work
Our four values aren't aspirational. They translate into concrete operating tenets that shape how we make decisions every day.
Beat Yesterday. We re-evaluate our own toolkit every quarter, because the workflows that made you effective three months ago may already be outdated. We act on 70% of the information rather than waiting for certainty. We timebox, execute, and iterate, getting work in front of real people quickly instead of polishing in isolation. And we own our output regardless of how we produced it. AI can get you 80% of the way there. Everything that goes out with your name on it is your responsibility to review, refine, and stand behind.
Embrace Type 2 Fun. We take on hard problems and find meaning in the work, even when it's uncomfortable. We assume good intent and follow up directly rather than venting in side channels. We celebrate wins loudly and specifically, naming the actual behavior so the whole team learns what great looks like. We are on by default, because we owe it to each other and to our customers to be available beyond what a regular corporate job would demand. And we do unscalable things today that build scalable advantages tomorrow: every hand-built account play and late-night proposal turnaround earns us the right to scale later.
Stay Hungry, Stay Curious. We expect everyone to be the expert on AI in their function before anyone asks them to be. The landscape changes weekly. When you learn something, teach it. Learn enough about adjacent functions to ask good questions. Ask the question you think is too basic, because in complex domains like benefits, the cost of a wrong assumption can be a lost account. And remember: curiosity without urgency is a hobby. Learning has to translate into action on a timeline that matches the opportunity.
Act Like an Owner. We say the uncomfortable thing early, raising missed commitments or quality gaps directly with the person within 48 hours. We surface problems when they're small, not the week they become a crisis. Customer-facing work gets priority, always. And we close the loop every time, because over-communication is a feature at our stage, not a bug.
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