THE SIGNAL
The Signal.
Practical guides for founders, hiring teams and technical people considering their next step.
Featured guide · Compensation
What does the right hire cost?
Explore salaries by city and specialism. See the sources, compare offers and put the whole package in context.
Explore the salary guide4 guides in AI & research
01
For founders
Define the founding engineer before you start the search.
A practical framework for separating technical delivery, product ownership and founder expectations.
Hire for the mandate. Then choose the title.
How to frame a CTO, Head of Engineering or engineering leadership search around the decisions that matter.
How to hire a founding engineer in New York
Define ownership, assess relevant work and explain the package before hiring a founding engineer for your New York startup.
Contract and interim technical hiring: define the gap first
A practical brief for contract and interim engineering, AI, research and leadership hires at startups and scaleups.
What competition results and founder experience can tell you
Use competition results and founder experience as context for engineering assessment, without treating achievements as proof of fit.
How to plan your first five engineering hires
Sequence early engineering hires around product needs, support and ownership rather than copying another startup’s organisation chart.
How to hire a founding engineer in San Francisco
Define the work, explain the trade-offs and make your first engineering hire in SF a considered decision.
How to hire a founding engineer in London
A London hiring brief that connects ownership, practical assessment and a package candidates can understand.
Closing engineering offers: why strong candidates decline at the final stage
Separate the reasons behind a declined offer, then use a clear checklist to make the next decision easier.
A week-by-week interview process for a seed-stage startup
A week-by-week plan that gives each interview a purpose, each decision an owner and candidates a clear next step.
On-site, hybrid or remote: what the requirement does to your candidate pool
Make the working requirement explicit, understand who it excludes and organise the team around it.
Your first engineering manager: when to hire and what to look for
Recognise the management work your team needs, then hire or promote someone with a clear mandate.
How to write an engineering job description people can respond to
Give engineers the scope, constraints and conditions they need to decide whether to talk.
Take-home task, live coding or paid trial: choosing an assessment
Choose the smallest practical assessment that answers the hiring decision, with clear time limits and AI rules.
Hiring engineers in two cities at once
Build one hiring plan across two cities, with explicit ownership, local packages and workable collaboration.
What to tell candidates about runway, funding and risk
Give candidates a clear, dated picture of the business, from the first conversation to the offer.
Reference checks for senior engineers: questions that reveal something useful
Use structured reference conversations to investigate technical ownership, collaboration and judgement.
02
For candidates
Before you join a startup, ask about the work.
A practical guide to understanding the role, the team and the trade-offs behind your next move.
Moving from Big Tech to a seed-stage startup: what changes
Test the work, package and support behind a seed-stage offer before leaving a larger technology company.
Negotiating a startup offer without damaging the relationship
Make a clear, considered offer request without turning the conversation into a contest.
Reading a startup before you join: funding, runway and team signals
Build a clear picture of the company from dated public evidence and direct conversations with the team.
Founding engineer or early engineer: what the title means for scope and equity
Understand the decisions, support and ownership behind an early engineering title.
Your first 90 days at a seed-stage startup
A practical first-quarter plan for learning the business, shipping useful work and earning clear ownership.
03
AI & research
AI engineer, research engineer or research scientist?
Start with the problem, the expected output and the way success will be evaluated.
Hiring AI engineers in 2026: what to test when everyone lists LLM experience
Test the engineering judgement behind an AI feature, from evaluation design to failure handling.
Hiring for applied AI: production experience versus prompt-level experience
Find the evidence of production ownership behind an AI title, portfolio or list of tools.
Research engineers at startups: do you need one yet?
Separate an unresolved research problem from a product or infrastructure problem before you hire.
04
Compensation
What does the right hire cost?
Explore salaries by city and specialism. See the sources, compare offers and put the whole package in context.
Salary, equity and bonus: comparing London, New York and San Francisco
Compare employer-advertised compensation in London, New York and San Francisco without confusing currency, seniority or equity.
Evaluating a startup equity offer: the questions behind the percentage
Turn an equity headline into terms you can understand, compare and ask questions about.
05
Choosing a recruiter
Technical recruiters for New York startups: a comparison
Compare recruitment models and specialisms for New York startups. Official sources, clear selection criteria and questions to ask before a search.
Technical recruiters for San Francisco startups: a comparison
Compare recruitment models and specialisms for San Francisco startups. Official sources, clear selection criteria and questions to ask before a search.
Technical recruiters for London startups: a comparison
Compare recruitment models and specialisms for London startups. Official sources, clear selection criteria and questions to ask before a search.
When to use a technical recruiter, and when not to
A practical decision framework for direct hiring, specialist searches and choosing the right recruitment arrangement.
AI & research
Define the work behind AI engineering, machine learning and research roles.