10+
Years in practice

Engineering, data, machine learning, security, and product people, hired directly onto your team and never as contractors.
Technical hiring is the part of your plan where one wrong hire is felt in the roadmap for a year.
SecureVision recruits technical teams for venture-backed tech companies, from seed through enterprise. Our deepest specialty is growing tech companies, and within that, technical and go-to-market hiring are our strengths: roughly half of our business is technical hiring.
We hire people directly onto your team, on your payroll, as employees.
We fill roles across the whole technical function.
Engineering and product leadership searches usually run through Retained Search or Executive Search.
That is the first question every technical buyer asks, and it deserves a straight answer.
Technical recruiting is a distinct skill set, built through volume and years rather than engineering depth. The first screen is not a technical evaluation, so nobody is put through a technical assessment at that stage. What the recruiter needs is a precise understanding of what you are looking for, and the pattern recognition that comes from interviewing engineers across many companies and stages.
That pattern recognition shows up as market knowledge. A leadership team once handed our recruiter a list of prestigious companies to source a senior Python backend engineer from. She gathered the data to show what she already knew from experience: the engineers at the top of that list mostly did not write Python, and the search would have burned a quarter before anyone noticed. Knowing which companies actually produce which skills is what stops that happening.
The recruiter-led first screen exists to protect your team. Engineers should not be carrying their own sourcing and first screens. Response rates on technical cold outreach are low, so one hire takes a great many attempts, and the rejections for interest, logistics, and cultural fit sit on top of that. We carry the sourcing, the logistics and the cultural fit. Your engineers evaluate technical fit and nothing else, and the measure of whether it is working is the pass-through rate from first screen to hiring-manager screen.
The method below is how we run every search. What changes on a technical search is what each step is looking for.
Over-indexing on pedigree. Recruiting engineers from a handful of famous companies without understanding their teams' technical skill set is limiting. Sourcing from a specific company is right when you can say why, because you know how they build, and that is the engineering identity you want. It is wrong when the reason is only that the company is good, and it costs you two to three months that you could have spent hiring someone who takes work off the team.
Chasing a candidate who does not exist. A hiring manager who has done everything writes a role for someone who has also done everything, even though most engineers haven’t had the chance to build that range.
Not deciding. Your team speaks to many candidates, runs them through the interview process, and still doesn’t hire. That is fear of missing the perfect candidate at the cost of an excellent one, and it burns roughly six months, by which point the team realizes it could have already hired several of the people it interviewed.
The engineering role is being actively reshaped and every team is taking a different approach. Some large companies now mandate AI coding, and most of the code an engineer there produces is written with it. That changes what good looks like.
In our view, the industry is moving toward engineers who are strong system designers and architects, who can direct AI to build sound code on top of their architecture, and jump in to add technical depth. Reasoning, problem-solving, and architectural judgment are becoming the markers, and each company’s own AI policy determines which secondary skills matter. A playbook written before this shift will not read the market it is hiring into.
The return-to-office push collided with an exodus of experienced talent from core markets, and in our experience, that has made top talent harder to find in the cities companies are asking people to return to.
More outreach into thinner markets is not the answer to that. More than a decade of relationships is, alongside a recruiting team distributed across a dozen-plus U.S. markets. We work with remote-first companies and with teams that want people in a specific city, and we run engagements outside the United States for customers with a global footprint.
10+
Years in practice
Hundreds
Organizations served
96%
Customer satisfaction, 496 reviews on G2
50%
Up to 50% reduction in time-to-fill
3-7DAYS
Initial recruiter screens
30-60DAYS
Time-to-fill
Five ways, and we will tell you which one fits.
Embedded Recruiting / RPO: two names for the same service at SecureVision. Our recruiters work as part of your team, in your systems, on your hiring plan. Contingent Search for a single role where you want a partner working alongside your team. Retained Search for hires where depth of assessment matters more than speed. Executive Search for engineering and product leadership. U.S. Expansion Recruiting for companies building a new market.
No hiring volume is too small for us to work with because our engagement models adapt to fit your needs. No engagement is too big either: we build custom talent solutions around ambitious and large-scale hiring targets.
For a first screen, yes, and that is the only screen we run. Technical recruiting is a skill built on volume and pattern recognition: knowing which companies produce which skills, what a given stage needs, and which questions separate a strong engineer from a well-rehearsed one.
No. We hire people directly onto your team as employees.
After the hiring-manager interview, not before. Asking a strong engineer for hours of work before they have spoken to anyone doing the job is the most reliable way to increase drop-out. We recommend a take-home they are encouraged to use AI on, then a live review with one of your engineers where they explain their approach and build a couple of iterations live.
Effectively every production language and framework in serious use, including Java, Python, Go, Ruby on Rails, Elixir, and the JavaScript ecosystem from React through Node.
It moves the emphasis toward system design, architecture, and reasoning, and away from whether someone can produce a block of code unaided. We screen for how an engineer directs AI and whether they understand what comes out of it, and we calibrate to your own policy on AI use.