Covering VP of Engineering, Engineering Manager, AI/ML and data leadership, software engineers, and privacy engineers, plus compensation, timelines, and how Beacon runs a search.
This page grows as we run more searches. It's not a complete record of everything we've ever done, just what we can document in full.
How specialist engineering recruiting works, and why it's different from a job board.
Engineering recruiting focuses specifically on technical roles: software engineers, engineering managers, VPs of Engineering, and specialists like AI/ML or privacy engineers. Unlike general recruiters, engineering specialists can evaluate real technical depth, not just keyword-match a resume.
A generalist recruiter screens for keywords and years of experience. A specialist pushes candidates on the specific systems they built, the tradeoffs they made, and verifies that with references from former managers.
A recruiter takes a detailed brief on the role and stack, builds a target candidate profile, sources directly rather than waiting for applications, runs technical screens and coding assessments, and delivers a shortlist of vetted finalists.
Contingency recruiting means you pay no fee unless a candidate is placed and starts the role. The recruiter absorbs the sourcing and screening cost upfront and only gets paid on success.
Through direct outbound sourcing, technical communities, referrals from placed candidates, and in some cases inbound applications. The strongest candidates are rarely browsing job boards.
Active candidates are job searching now. Passive candidates are employed, performing well, and not looking. The strongest engineering hires are almost always passive, reached through direct outreach.
A talent map is a structured view of the candidate landscape for a specific role: who is in the market, what they earn, and how accessible they are, so you can set realistic comp and timeline expectations before a search starts.
VP of Engineering Hiring
What the role does, why it's hard to fill, and what it costs.
Owns the engineering organization end to end: hiring and developing the team, setting architecture and delivery standards, and aligning engineering output with company priorities.
Strong ICs don't automatically make strong leaders, and strong managers aren't always technically credible. Genuine candidates need real technical depth, a track record building teams, and judgment on when to delegate.
On Beacon's own recent search, a VP of Engineering placement closed at $300K total comp. AI/ML and data leadership skills are commanding a premium on top of standard VP-level comp right now.
An Engineering Manager typically owns one or two teams. A VP of Engineering owns the whole organization, including the Engineering Managers, and is accountable for org-wide architecture and hiring strategy.
Our own VP of Engineering search ran 212 days, working through a pipeline of 547 candidates down to 5 real finalists. A genuine VP-level search takes real time.
Ask them to walk through a real scaling decision, test how they think about technical debt versus velocity, ask about a bad hire they made, and check references specifically on team retention.
Engineering Manager Hiring
The player-coach role, and what makes it hard to fill.
Usually a player-coach role: owning one or two teams' delivery and growth, mentoring engineers, staying hands-on with code, and often partnering directly with the CEO or CTO on technical direction.
Most candidates lean one way: strong ICs who've never built a team, or managers who've stopped coding and lost technical credibility. A genuine player-coach who can do both is rare.
On Beacon's own recent search, an Engineering Manager placement closed at $300K total comp, in a market where the candidate had competing offers from $270K to $500K depending on stage.
Our own search closed in 59 days by identifying one exact-fit candidate early and pursuing them directly, rather than running a wide, shallow funnel.
A Staff Engineer is a senior IC with broad technical influence but no direct reports. An Engineering Manager owns people management and delivery, even at a player-coach startup where they're still writing code.
AI/ML & Data Leadership Hiring
The hottest, hardest-to-fill technical hire in the market right now.
53% of US tech job postings now require AI or ML skills, up from 29% a year earlier, and AI/ML and data science postings grew 163% year over year.
Commonly leading a small team that turns raw data into analysis- or model-ready datasets, owning data quality and pipeline design, and in regulated industries, building compliance into the process.
On Beacon's own recent search, a Clinical Data Scientist Manager role closed at $185K total comp. Roles in this category are commanding a real premium as demand keeps climbing.
Our own search closed in 43 days, the fastest of any engineering search we've run, when the requirement was well-defined and worked directly.
Yes, alongside core software engineering and specialist roles like privacy engineering, reflecting the current market where AI-native technical talent is the hardest hire for most SaaS companies.
A Data Scientist typically focuses on analysis and modeling. An ML Engineer focuses on building and operating production systems that serve models at scale. Leadership roles increasingly need fluency in both.
Software Engineer Hiring
Senior ICs and specialist engineers, generalist and beyond.
Senior individual-contributor engineers, often with a specialization: data-intensive applications, ETL pipelines, cloud infrastructure, and privacy or compliance-adjacent engineering.
A specialist role narrows the pool sharply. Our own specialist search ran 105 days and drew 884 total candidates before landing the right one.
On Beacon's own recent search, a senior software engineer specializing in data privacy closed at $150K total comp.
Push for the specific systems a candidate built end to end, run structured technical assessments, and coordinate real interview loops including coding exercises with the hiring team.
A generalist search screens for broad technical competence. A specialist search narrows to candidates with a specific, verifiable background, usually meaning a smaller pool and a more targeted sourcing approach.
Privacy Engineer Hiring
Data de-identification, regulated data, and why sourcing usually starts with outbound.
Builds and operates systems that remove protected or personally identifiable information from data at scale, particularly in healthcare and other regulated industries.
Direct de-identification experience is genuinely rare. Our own search started as a targeted outbound sourcing sequence on LinkedIn, before any job posting went live.
On Beacon's own recent search, a privacy engineer role at a healthcare AI company closed at $140K total comp.
Direct hands-on experience working with de-identified data, real Python and SQL skill, and comfort operating in a regulated, high-stakes environment.
Healthcare data carries real compliance stakes. Companies working with clinical data at scale need engineers who've handled de-identification before, not general-purpose backend engineers.
Engineering Compensation and Benchmarks
What real recent searches have paid, and why.
It varies sharply by specialization. On Beacon's own recent searches, engineering placements ranged from $140K total comp for a senior IC specialist up to $300K for VP of Engineering and Engineering Manager hires.
Demand for AI/ML and data skills grew far faster than the supply of engineers who can credibly do the work, with 53% of US tech postings now requiring AI or ML skills, up from 29% a year earlier.
On-site roles in major tech hubs like New York and San Francisco typically command a premium over remote-friendly roles at similar seniority, though strong specialist skills can close that gap.
It depends on the candidate's own risk tolerance and career stage, but for a genuinely scarce leadership hire, being flexible on the mix often matters more than the headline number.
Typically base salary plus any target bonus and the annualized value of equity. Figures cited on Beacon's engineering pages reflect total comp on actual closed searches, not base salary alone.
Hiring Process and Timelines
What actually determines how long a search takes.
On Beacon's own documented searches, timelines ranged from 43 days for a fast, well-defined AI/ML leadership hire up to 212 days for a VP of Engineering search.
A specialist requirement narrows the qualified pool sharply. Our largest funnel, 884 candidates, was actually a specialist search, not a generalist one.
A structured technical assessment or coding exercise, a real conversation with the hiring manager or a senior engineering leader, and reference checks that ask specifically what the candidate built and owned.
It depends on the search. One of ours produced 5 real finalists from a pipeline of 547. Others were narrow and high-conviction, built around one exact-fit candidate identified early.
Slow interview feedback, too many rounds, and requirements that shift mid-search are the most common causes. Staying decisive once a strong candidate is in process keeps timelines tight.
Working with Beacon Talent
How the engineering practice fits alongside Beacon's broader recruiting work.
No. Beacon's core practice is GTM recruiting, and we also run a dedicated engineering practice covering VP of Engineering, Engineering Manager, AI/ML and data leadership, software engineering, and privacy engineering searches.
Documented placements include a VP of Engineering, an Engineering Manager, AI/ML and data leadership, a software engineer, and a privacy engineer. These are the searches we can document in full; we've run engineering searches beyond what's captured here too.
We push past the resume for the specific system a candidate shipped end to end, and several of our searches have covered healthcare AI and regulated clinical-data environments, where the technical bar and the compliance bar are both real.
Yes. Multiple documented engineering searches have been for healthcare AI companies working with clinical data at scale, including privacy engineering and AI/ML leadership roles.
Yes. Book a free 15-minute call and we'll deliver a custom talent map for your search, qualified candidates matched to your stack and stage, comp benchmarks, and market difficulty, within 48 hours.
Ready to start your search?
Book a free 15-minute call. We will confirm role fit, discuss your ideal candidate profile, and deliver a talent map within 48 hours.