New York, United States
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Remote · United States
Tucson, United States
Long Beach, United States
$230K–$300K/yr
Published by the employer
We are looking for a Senior Applied AI Engineer to join the Agent Runtime team behind Alwin, our new Agentic Automation Platform for audit and finance. Alwin agents run for long times, work through multi-step audit procedures over client evidence, and return finished work papers with every number traced back to source. Humans review and sign off.
The runtime is the layer every agent depends on: the model access, the base system prompt and context, the tools (document extraction, retrieval, sandboxes, MCP integrations), the orchestration of agents and sub-agents, and the observability and evals that tell us whether an agent is performing to objective standards. You will own the applied AI half of that layer. Product teams build audit-specific agents on top of it; you decide how the base agent reasons, what tools it gets and at what abstraction, how it manages context over long horizons, and how we measure and hill-climb accuracy, speed and cost.
This is a hands-on role in a small team with a large blast radius. Your work goes in front of hundreds of thousands of audit and finance professionals, and the problems are largely open: there is no playbook for production agents in a regulated domain, so you will help write it.
DataSnipper is the Agentic Automation Platform for audit and finance. Known worldwide for our Excel add-in, we are now building Alwin by DataSnipper: purpose-built agents that execute audit and finance workflows end-to-end, with every output traceable back to source evidence and a human signing off. Headquartered in Amsterdam with offices in New York, Tokyo, Kuala Lumpur and Sydney, we are used by hundreds of thousands of professionals at the world's largest firms. Our mission: automate the mundane, unlock the meaningful.
Own the base agents: system prompt, context engineering, memory and state management for tasks that span many turns and hours of execution
Design how agents use the tool layer, including document extraction, retrieval, code and file sandboxes, and MCP integrations, choosing the right level of abstraction so agents handle edge cases without wasting effort on mechanical steps
Implement agentic patterns such as sub-agent composition, planning modes, human-in-the-loop gates, and model routing across multiple providers
Ship end-to-end: from prototype with our audit domain experts, through evaluation, to production on Alwin
Define and build automatic processes that improve the agent continuously over time
Keep costs under control and implement cost-efficient approaches to AI-centric workflows
Extract signal from long agent trajectories: attribute outcomes to specific reasoning steps and tool calls, classify failure modes, and turn them into fixes
Hill-climb accuracy, latency and token cost, and make the trade-offs explicit for the teams building on the runtime
Instrument agent behaviour with our observability stack and use production traces to drive improvements
Partner with Agent Experience teams, Document Intelligence and the AI Platform team to turn their needs into runtime capabilities that are self-service rather than a request queue
Stay on the frontier: evaluate new models, techniques and agent patterns, and bring the ones that hold up into production
5+ years of software engineering experience with strong, production-grade Python
Experience shipping and operating an LLM-powered product in production: you have dealt with hallucinations, latency spikes, tool failures and cost explosions at scale, and can explain what broke and how you fixed it
Hands-on experience building agentic systems: control loops, tool selection, planning versus execution, retries and fallbacks, not only prompt-and-parse pipelines
Experience with evaluation: you have built datasets, run offline and online experiments, and used the results to make an AI system measurably better
Fluency with LLM APIs and agent frameworks across more than one model provider
Proficient with AI-assisted engineering and excited about working with coding agents daily
Strong fundamentals in software architecture and system design, and a track record of reliable, well-tested delivery
Excellent communication; you can work directly with auditors and product partners to define what "correct" means
Experience with Durable workflow engines or long-running background task systems
Experience with RAG and retrieval pipelines over large, messy document corpora
Document AI: VLMs, OCR, structured extraction and their metrics
Sandboxed code execution, MCP, or multi-agent orchestration in production
Domain experience in audit, accounting or fintech
Familiarity with OWASP GenAI security practices and working in a regulated, privacy-sensitive environment
Ownership: You own work end-to-end, anticipate issues, and ensure high-quality delivery with minimal support. We expect you to influence the technical direction of the team.
Growth Mindset: You encourage open feedback exchange and provide clear, balanced feedback that helps others grow
Collaboration: You build strong cross-functional relationships and influence peers through expertise, data, and empathy
Adaptability: You navigate ambiguity calmly, model positive behavior, and help peers adjust through clear communication
Judgment: You exercise sound judgment in ambiguous situations, balance speed and accuracy, and adjust priorities proactively
Excellent salary + stock participation plan
Flexible paid time off
Comprehensive medical and dental coverage
401K match
Paid parental leave
Company-sponsored lunch
Hybrid mode of work (at least 3 days onsite in our New York City office)
Being part of one of the fastest-growing scale-ups in the Netherlands
Make an impact by disrupting the finance industry with us
A flexible and growing organization with lots of opportunities to learn and develop
International working environment, with a team of friendly and driven colleagues
Access to OpenUp and Talkspace, the mental health and wellness platform
Multiple social activities for team building 🤩
Recruiter screen
Hiring Manager interview
Peer programming session
System design interview
Final interviews with Engineering leadership