Forward Deployed Engineer II (Bulgaria)

Handshake was founded on a simple belief that everyone deserves a path to a great career, regardless of where they went to school or who they know. Today, we power 25 million job seekers, 1 million+ employers, and 1,600 educational institutions.

In 2025, we started Handshake AI and built the fastest-growing AI data business in history. We work directly with frontier AI lab researchers to create evaluations, publish benchmarks, and push the boundary of data. We’ve grown from $0 to ~$1B run rate and pay ~$60M to over 30K individuals every month.

Why join Handshake now:

• Shape how every career evolves in the AI economy, at global scale, with impact your friends, family, and peers can see and feel

• Partner hand-in-hand with world-class AI labs, Fortune 500 partners, and the world’s top educational institutions

• Work together with engineers, scientists, operators, and more from Palantir, Meta, Scale AI, and former YC founders

• Build a massive, fast-growing business with billions in revenue
  

Human data is the core infrastructure to AI advancement. Frontier AI labs currently improve model capabilities with various data-intensive post-training techniques. We believe that data spend for AI training will increase by 3-5x in the next few years and continue for much longer as models take on new domains. Handshake AI supports all of the frontier AI labs, working on their most complex data at the largest scale.

• Own the design, architecture, and delivery of tailored, production-grade solutions for complex, high-impact customer needs — often with no existing playbook

• Spot the problem two weeks before it becomes a blocker — a fragile pipeline, a missing quality check, a system that will not survive the next customer commitment — and make the case for what to build

• Build the tools and internal systems the wider team knows it needs but has never had time to build

• Own the tradeoff conversation: this is two engineering weeks, here is what it unlocks, here is what it costs, do we need it now? FDEs weigh engineering effort against business outcomes weekly, not once a quarter

• Turn one-off fixes into reusable infrastructure and repeatable deployment patterns, so the same problem does not need solving again for the next customer

• Make key architectural decisions around reliability, scalability, and security, and create the playbooks and documentation that let the team scale deployments efficiently

• Mentor and uplevel other engineers through code reviews, architectural guidance, and the best practices you establish for forward-deployed work
  

We will also be straight with you about the pace. FDEs own deliverables that customers are waiting on, and the projects carry meaningful revenue, which means real ownership and real pressure. The work is demanding, and the tradeoff is scope: you will touch more systems, more customers, and make more decisions than a product role would. Several years of engineering growth compressed into one is no exaggeration.

You will work alongside staff-level engineers and, on some projects, directly with the founders. You will also be in rooms where spending and build-vs-buy decisions get made — conversations most engineers see a handful of times a year.

• 6+ years of professional software engineering experience

• Strong proficiency with ReactJS and TypeScript, with a high bar for code quality, usability, and accessibility

• Deep understanding of relational databases (e.g., PostgreSQL), data modeling, and system design

• Experience with cloud infrastructure (AWS, GCP), deployment pipelines, and production operations at scale

• A demonstrated ability to lead technical projects end-to-end in ambiguous environments with shifting requirements

• A track record of mentoring engineers and raising the bar on engineering practices within a team

• Excellent communication and stakeholder management — you communicate as you go, saying what you think the work is, where it gets complicated, and how long it will take, so course corrections happen early rather than after a week of heads-down work

• Innate curiosity: you ask the question everyone in the room was thinking, and you keep asking until you have the context needed to build the right thing

• You use AI heavily and critically — as a tool you understand and can check, never a substitute for your own understanding of the problem

• You create work rather than wait for it. If you prefer a manager handing you the next ticket, this is not the right role

• You know when a problem is not an engineering problem, and you are willing to say so
  

• Experience architecting and delivering production systems for LLMs or other AI-powered products in real-world, customer-facing environments

• Background in building or contributing to component libraries, design systems, or reusable deployment frameworks

• Proven experience with performance optimization and scaling of full-stack applications under real-world constraints and high-traffic conditions