Sorry for the missed edition last week. I was on the company incentive trip in Las Vegas and completely dropped the ball on sending. This week covers the last two weeks and focuses on the single thing that keeps deciding outcomes in Python hiring: speed.
The signal: candidates read your hiring speed as your operating speed
A slow loop does not just lose candidates. It signals indecision, weak execution, or internal friction.
A few numbers worth keeping on a sticky note:
62% of professionals lose interest if they hear nothing within two weeks, rising to 77% if there is no update within three weeks.
44% of jobseekers would reject a job offer if the hiring process took 4–8 weeks, and another 45% would bow out if it took longer than 8 weeks.
Time-to-hire fell hard in 2020–2022, then climbed again. Indeed’s Hiring Lab found time-to-hire fell 23% from Feb 2020 to Aug 2022, and has risen since, nearly back to Jan 2019 levels as of their analysis.
In Ashby’s benchmark data, 2023 had an average offer acceptance rate of 81%, and “time in offer stage” dipped 16% in Q3 2023 from around 3 days to 2.5 days, suggesting candidates were making decisions faster.
GoodTime’s 2025 Hiring Insights notes 60% of companies reported time-to-hire increased, and TA teams hit only 47.9% of their hiring goals in 2024.
A real example from my desk
I once had a candidate with serious startup experience reject an offer instantly because it arrived five days after the final interview. Their reasoning was blunt: if a startup takes five days to send an offer, that’s probably the speed they operate at everywhere else.
That’s the real point. In startups especially, hiring speed is interpreted as a proxy for product speed.
A speed playbook for hiring managers (built for technical hiring)
1) Set a simple SLA per stage
If you do nothing else, set targets and hold yourselves to them:
Time-to-schedule: first interview booked within 48 hours of greenlighting the candidate
Time between stages: next step confirmed within 24 hours of passing
Final to offer: decision same day, offer sent within 24–48 hours
This is not about being “nice”. It is about not losing candidates who are already in motion.
2) Debrief same day, every time
If your panel interviews finish at 5pm, do the debrief at 5:15pm. If you wait, you drift.
3) Send the “intent to offer” message fast
Even if the written offer needs approvals, send a quick note within 24 hours: “We’re aligned, we’re issuing the offer, here’s the timeline.” It prevents the candidate from mentally closing the loop.
4) Treat the offer as a project deliverable
If engineering can ship a patch quickly, talent teams can ship an offer quickly. The bottleneck is usually internal ownership, not complexity.
For Python developers: how to manage multiple processes without burning bridges
If you have more than one process running, your job is to create clarity and keep optionality.
Ask every company for their timeline upfront: “When is final stage, when do offers go out?”
When you have momentum elsewhere, say so early and neutrally: “I’m progressing with another process and they’re moving quickly.”
If you want to accelerate a company you prefer, give them a clean deadline: “If you can confirm next steps by Thursday, I can keep this moving.”
Candidates do not lose offers by being transparent. They lose offers by going quiet.
Quick Python watch (last two weeks)
FastAPI 0.135.2 released Mar 23, 2026.
Ruff 0.15.7 released Mar 19, 2026.
uv 0.10.12 released Mar 19, 2026.
Python 3.15 JIT progress: Python Insider reports the 3.15 alpha JIT is about 11–12% faster on macOS AArch64 vs the tail-calling interpreter, and 5–6% faster than the standard interpreter on x86_64 Linux (preliminary geometric means).
Python 3.15.0a7 notes the JIT has been significantly upgraded, including 3–4% geometric mean improvement on x86_64 Linux and 7–8% speedup on AArch64 macOS vs the tail-calling interpreter.
Role of the week
Senior Python Engineer | YC-backed AI voice agents | London | 5 days onsite
A YC-backed AI voice agent startup in London, building agent workflows end to end (voice is the interface, agents are the core). Team is ~25 people and they’re aiming to hire 20 engineers this year. Series A stage.
What they want
Strong Python fundamentals
Async programming (the difference between “works in a demo” and “holds up in production”)
AI agents experience (does not need to be voice specific)
Comfort building reliable systems that run continuously
Process (4 stages)
Intro screen
Technical round
System/agent design
Final onsite
Comp
£100k to £250k base (depending on level)
If you want a fast-moving team, high output environment, and a role where engineering speed matters, this is a strong one.
Hiring? Contact
Josh Smith
LinkedIn: https://www.linkedin.com/in/python-recruitment/
Email: [email protected]
Phone: 01727 225 552