About Neo
AI is the most significant shift in how work gets done since the advent of the Internet — yet most organizations fail to capture its value. Neo is changing that.
Founded by Bhavin Turakhia — Co-Founder of Zeta, Radix, and Titan — Neo is building an integrated, AI-first suite of products that capture work, centralize context, and make AI a first-class participant in every workflow:
Tasket — A radically reimagined, AI-native work management platform that centralizes context and makes AI delegation seamless for all work.
Friday — AI Assistant, co-worker and agent platform, pre-integrated into Tasket, Studio, Drive and 1000+ SaaS platforms.
Studio — AI-native suite for co-creating docs, spreadsheets and diagrams collaboratively with humans and agents.
Scribe — AI assistant for meetings that goes beyond note-taking and actually does your work while on the call.
About the role
As a Principal Engineer II you will set the technical direction for the Neo suite and mentor senior engineers across teams.
We are proponents of leveraging best-in-breed AI tools at every step of the engineering lifecycle — design, development, review, and testing. This requires a different engineering mindset: a lean team of senior, hands-on, AI-adept engineers, resulting in ultra-rapid iteration cycles and fast output. You will own, oversee, and set the bar for this approach across the suite, from design through to production.
This is a role for an independent decision-maker who makes architectural calls that span products and are hard to reverse, and who raises the bar for what both people and agents ship.
Responsibilities
Architecture & technical direction
Own the architecture of the Neo suite across backend and frontend — service boundaries, data models, and the shared platforms every product builds on.
Set multi-year technical direction for the suite, and make the build-vs-buy, vendor, and model decisions behind it.
Own how agents act inside the product — tool and MCP integrations, context and permissions, and the cost and latency of model calls at scale.
Resolve the cross-cutting reliability, performance, and security concerns that no single team owns.
Execution & delivery
Drive delivery end to end: break a spec down, run agents in parallel to implement, and steer them to a working result.
Own verification — the real bottleneck in an agentic workflow. Decide what "correct" means for a system and make sure what ships meets it.
Lead the highest-risk work across teams — migrations, re-architecture, major incidents — through to production.
AI-first SDLC harness
Build and improve the harness engineering uses — specs, reusable skills, subagents, evals, and guardrails that let many agents work in parallel with consistent results.
Set the org-wide standard for agent use: what agents may change, and the quality gates their changes pass before a human reviews them.
Mentoring & raising the bar
Mentor senior engineers and run design reviews; turn what works into standards and paved roads.
Spread good agent practice across teams so its leverage compounds, not just your own.
Product thinking
Work with product to pin down the right problem before building — scope feasibility, surface trade-offs, and push back when the cost isn't worth the outcome.
Own engineering outcomes across the suite: reliability, cost to serve, and delivery speed.
Skills
AI-native engineering practice — Fluent in an agentic workflow: spec-driven delivery, steering coding agents, and building the harness that makes their output reliable. Experienced in defining how an engineering org uses agents.
AI system architecture — Agent orchestration, tool and MCP integration, context and memory design, and evals in production; managing LLM cost, latency, and failure modes.
Language & runtime fundamentals — Deep proficiency in one or more of Java, Python, TypeScript/JavaScript, or Go, including concurrency, memory management, and runtime behaviour under load. Picks the right language for the job.
System internals — Working knowledge of processor, memory, network, and storage internals, and how to write code that exploits hardware well and avoids bottlenecks.
Distributed systems — Fault modelling, concurrency, isolation, and consensus; time, clocks, and ordering of events; rate control and load distribution. Strong design and problem-solving instincts for performance, scalability, security, and reliability.
Scale & multi-tenancy — Multi-region, multi-tenant SaaS, designed for tight p99 latency and low cost to serve.
System internals & performance — Processor, memory, network, and storage internals; non-blocking I/O; diagnosing memory issues, GC tuning, and resource leaks.
Troubleshooting & performance — Diagnosing memory issues, GC tuning, and resource leaks to keep systems stable and efficient under load.
Core infrastructure — Hands-on with the internals of systems such as Kafka, Cassandra/Scylla, and Redis — enough to troubleshoot, tune, and make sound usage decisions.
Orchestration & protocols — Kubernetes and containerised deployment on AWS, Google Cloud, or Azure; gRPC, HTTP/2, and QUIC.
Experience & Qualifications
12+ years in software development and delivery, with a track record of setting technical direction across multiple teams or products.
Led at least one large architecture change end to end — a migration, re-platform, or new core system.
Production experience across a modern stack — e.g. Java, Python, or Node/TypeScript — with REST, SQL (PostgreSQL or MySQL), messaging systems, and microservices.
Engineering degree in computer science or equivalent experience.
Equal Opportunity
Neo is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We encourage applicants from all backgrounds, cultures, and communities to apply and believe that a diverse workforce is key to our success.