The Context
Hi, I'm Logan, and I lead Talent at Alembic. I want to tell you about the hardest search we run, because it's the one you'd own first. Alembic's science team implements causal mathematics — the algorithms our whole product rests on — directly in CUDA and C++. The people who can do this work are engineers who went and got a PhD, or PhDs who then engineered for years. They are not on the first page of any LinkedIn search. They're in labs, HPC groups, quant shops, and PhD-exit communities, and most of them aren't looking. Finding and genuinely engaging them is a research problem, a writing problem, and a craft problem all at once. If that paragraph excited you rather than tired you, you're who I'm looking for.
The wider context: a $145M Series B, a company expecting to roughly double in the next year, and a hiring plan that's mostly technical. Sourcing is the bottleneck on the hardest of those searches, which makes this seat one of the highest-leverage in the company.
How we work, briefly: structured hiring with scorecards before searches; speed as our competitive advantage; an operating loop of Learn → Build → Share → Repeat; and an AI-native practice — we experiment with new sourcing tools constantly and expect to find alpha before our competitors do. We wrote our playbook down before we wrote this ad, and we share it openly here.
I came to Alembic because the founders believe what I believe: investing in people is how you build the company, not a cost of building it. Done right, that buys three things, in order — a team that demands excellence, work that carries daily personal meaning, and a real shot at generational wealth for the people who build it. I haven't often found all three in one place.
About Us
Alembic is the pioneering Causal AI platform. We help the world's largest enterprises move past correlation to prove what actually drives business outcomes — the question marketing and growth teams have never been able to answer with confidence. Fortune 100 companies including Nvidia, Delta Air Lines, and Mars use Alembic to make multimillion-dollar decisions on trusted, causal evidence.
We're backed by a $145M Series B from WndrCo (founded by Jeffrey Katzenberg), Jensen Huang, Joe Montana, Prysm Capital, and Accenture. Our models run on our own NVIDIA DGX SuperPOD built on Grace Blackwell infrastructure — one of the fastest private supercomputers in the world. (We've melted GPUs getting here.)
The Role
Your mission: build and qualify pipelines for our hardest technical searches, so that every priority role carries a healthy top-of-funnel of closable, calibrated candidates.
You'll own top-of-funnel for engineering and science searches end to end — market mapping, channel development, outreach, and first qualification — working directly with the rest of the Talent team and with the hiring managers whose searches you carry.
Success in the first six months looks like:
You've mapped the market for our research-engineer search — across labs, HPC, quant, and PhD-exit channels — and turned it into living pipeline, validated with hiring managers.
Your outreach converts. Personalized, content-led campaigns that prospects actually answer — and that turn into screens, not just replies.
Hires are landing that began as your sourced candidates.
You've built infrastructure that outlasts any one search: talent pools, nurture sequences, clean tracking, channels we didn't have before you.
What Will Help You Succeed
3+ years of technical sourcing on genuinely hard searches — niche engineering, research, or infrastructure profiles.
Research craft beyond LinkedIn: GitHub, papers, patents, conference talks, communities. You find people who can't be found by filter.
Outreach writing that demonstrably converts — you can show us the messages and the numbers.
A data-driven practice: you optimize your own funnel by root cause and can walk us through a time you diagnosed and fixed one.
You prioritize your own workload without supervision.
An AI-native sourcing stack: you experiment with semantic search, enrichment, and automation tools, and you're constantly hunting for alpha.
Curiosity and willfulness, in equal measure.
This role is probably not for you if:
Your sourcing is LinkedIn Recruiter plus saved Boolean strings, full stop.
Volume sequences are your strategy — you can't personalize at quality, or don't want to.
You need requisitions pre-qualified and target profiles handed down before you can start.
You see sourcing as something to escape rather than a craft to master first. (This seat can grow into full-cycle recruiting as we scale — but it starts with wanting to be great at this.)
Five days a week in our San Francisco office isn't workable for you. The calibration conversations that make sourcing precise happen in the room.
Why You Might Be Excited About Alembic
Searches with real degree-of-difficulty: CUDA/C++ research engineers, supercomputer SREs, data-platform builders. Work that makes you better, for stakes that matter — the company's growth plan runs through the searches you build.
A story engineers actually answer outreach about: Causal AI for Fortune 100 companies, mathematics running on one of the world's fastest private supercomputers. Your messages get to be interesting because the truth is.
AI-native by design: We'll fund your tooling experiments — semantic search, enrichment, automation. The recruiter-as-creator era is here, and this team intends to be evidence of it.
Define what great looks like: You're our first dedicated sourcer. The channels, pools, and playbooks you build become how Alembic sources — and a clear growth path opens as the function scales.
Series B momentum, real ownership: Meaningful equity at a Series B company that's raised $145M, with proven product-market fit and Fortune 100 traction.
Why You Might Not Be Excited
We're building the talent function while running it. You'll define process, not inherit it.
Brevity is strength here — feedback is direct, meetings are short, and writing matters. If you prefer a softer communication culture, this will feel sharp.
Priorities shift as we grow. We have real paying customers and a playbook, and we still move at startup speed at Series B scale.