Talent Acquisition Strategy — Draft

Building Oratomic's
talent engine.

Oratomic's next constraint won't only be scientific. It will be organisational — finding, understanding, attracting and closing the rare people who can turn fault-tolerant quantum computing from frontier research into a working system.

This is a proposed approach to building that function.

Prepared forDolev Bluvstein, Oratomic
Prepared byLee Sam
ContextExploratory follow-up
StatusDirectional, pre-calibration
01 — The Challenge

The talent problem is interdisciplinary.

Oratomic's hiring problem isn't "find quantum people." It's building a dense, high-trust network across several rare talent markets at once — and a recruiting system that's fluent in all of them simultaneously, not sequentially.

Frontier science

  • Neutral atoms
  • AMO physics
  • Quantum error correction
  • Fault tolerance

Precision engineering

  • Optics & lasers
  • Optomechanics
  • Electronics
  • Instrumentation

Software & AI

  • Lab automation
  • Scientific computing
  • AI for research
  • Control systems

Operating infrastructure

  • Lab operations
  • Technicians
  • Procurement & safety
  • Research cadence
The recruiting system has to be as interdisciplinary as the company.
02 — Talent Map

Where the talent will come from.

Six lanes, each fragmented across a different world — academia, quantum hardware companies, national labs, adjacent precision-hardware industries, and AI-for-science teams. The map below is a starting hypothesis, built to be sharpened after calibration with the team.

AcademiaNational Labs

Neutral atom / AMO experimentalists

Experimental physicists, postdocs, hardware builders
Hunting grounds
QuEra, Atom Computing, Pasqal, Infleqtion, planqc · Harvard, MIT, Caltech, JILA, Berkeley, Chicago, Oxford, Cambridge, Max Planck, NIST
Search signals
Rydberg atoms, optical tweezers, laser cooling, atom arrays, quantum optics
Quantum CosAcademia

Quantum error correction / theory

QEC theorists, quantum information scientists, algorithm researchers
Hunting grounds
Caltech IQIM, Waterloo IQC, Maryland JQI · Google Quantum AI, AWS Center for Quantum Computing, Microsoft Quantum, IBM Quantum, Riverlane
Search signals
qLDPC, surface codes, stabilizer codes, logical qubits, decoders, resource estimation
Adjacent Hardware

Optics / optomechanics / photonics

Optical engineers, laser systems engineers, instrumentation engineers
Hunting grounds
ASML, Zeiss, Coherent, Thorlabs, Newport/MKS, Hamamatsu, Oxford Instruments, Toptica, Menlo Systems
Search signals
Free-space optics, laser alignment, vacuum systems, interferometry, precision optomechanics
AI-for-Science

Software / AI / scientific systems

Research engineers, scientific software engineers, AI scientists, lab automation engineers
Hunting grounds
Google DeepMind, Google Quantum AI, Microsoft Research, NVIDIA, Isomorphic Labs, Recursion, robotics & lab-automation companies
Search signals
AI for science, experiment automation, active learning, Bayesian optimisation, data acquisition
National LabsAdjacent Hardware

Lab operations / technicians

Lab operations managers, technicians, R&D operations
Hunting grounds
Quantum & university labs, semiconductor tooling, robotics, aerospace, national labs, precision hardware companies
Search signals
Lab operations, optical technician, vacuum systems, EHS, calibration, procurement
Generalist

Exceptional generalists

High-agency engineers who work across hardware, software, physics and systems
Hunting grounds
SpaceX, Anduril, Tesla, Zipline, Varda, DeepMind, OpenAI, Anthropic, robotics, aerospace, elite academic labs
Search signals
Research engineer, hardware generalist, systems builder, puzzle solver, scientific instrumentation
Where Oratomic should show up — treated as intelligence-gathering, not employer branding
Quantum / QEC / AMO

APS March Meeting, DAMOP, QIP, TQC, IEEE Quantum Week, Q2B

Optics & photonics

SPIE Photonics West, CLEO, Optica events

AI-for-science

NeurIPS, ICML, ICLR AI4Science workshops

University channels

Caltech, Harvard, MIT, JILA, Berkeley, Oxford, Cambridge, Waterloo, Max Planck, national labs

03 — Operating Model

From founder-led hiring to a repeatable system.

The first version of this function shouldn't be bureaucratic. It should create clarity, speed and signal around the highest-leverage hires — with AI compressing the learning curve, not replacing judgment.

01

Calibration

Define excellence for each role family before sourcing begins.

  • Scorecards built with technical leaders
  • Benchmark candidates per search
  • False positives and adjacent profiles named early
02

Talent intelligence

A living map of the field, kept current — AI-assisted, not AI-decided.

  • Labs, papers, authors, co-authorship networks
  • Competitor hiring & academic movement tracked
  • Paper-to-profile signal extraction
03

Sourcing & engagement

Research-specific outreach, not generic recruiter copy.

  • Messaging tailored by talent lane
  • Advisor, investor and academic networks activated
  • Long-term relationships with postdocs & senior scientists
04

Assessment

A high bar, held consistently, with fast feedback loops.

  • Structured loops, clear technical ownership
  • Fast debriefs
  • Adjust the search, never the bar
05

Closing

Scientist-led storytelling, tailored to what each candidate needs to hear.

  • Why now, why Oratomic, why this team
  • Close strategy differs by academia / industry / generalist
  • The mission does the closing, not the offer letter
06

Infrastructure

Just enough system to stay fast as headcount grows.

  • Talent CRM & hiring dashboard
  • Search health metrics
  • Hiring-manager cadence & candidate experience
Inputs
Papers, profiles, labs, companies, roles, conferences
AI-assisted
Summarisation, classification, network mapping
Outputs
Calibrated pipelines, tailored outreach, hiring intelligence
AI accelerates research. Humans own judgment.
04 — First 90 Days

Clarity, then system, then momentum.

Days 1–30
Clarity

Learn and calibrate

  • Understand the technical roadmap and hiring priorities
  • Build the talent ontology
  • Sit in on technical screens and debriefs
  • Build benchmark candidate profiles
  • Map the first 200–300 target people, labs and companies
Days 31–60
System

Build the machine

  • Launch sourcing sprints by role family
  • Stand up search health dashboards
  • Build outreach messaging per talent lane
  • Establish a weekly hiring review
  • Define scorecards, loops, and advisor activation
Days 61–90
Momentum

Scale and improve

  • Improve conversion and candidate experience
  • Formalise the talent CRM and long-term relationship strategy
  • Build a conference and event sourcing calendar
  • Measure source quality and interview signal
  • Decide when to add recruiting capacity
05 — Why Me

Why I could help.

I wouldn't pretend to be a quantum expert on day one. My value is building the system that helps Oratomic identify, understand, attract and close the people who are — and the strongest candidates need to believe this is one of the few places their work can directly decide whether utility-scale quantum computing becomes real.

High humility. High agency. High standards.
  • Built Cloudflare's EMEA recruiting function from the ground up
  • Scaled hiring through high-growth years while holding the bar
  • Partnered with senior technical and business leaders on headcount planning
  • Built recruiting workflows and tools using AI
  • Executive search, market mapping, and high-signal sourcing
  • Comfortable learning complex technical domains quickly
  • Low-ego operator — listens, calibrates, builds around expert teams
06 — Next Step

The talent function as a force multiplier.

For Oratomic, recruiting isn't administrative — it's a force multiplier for the scientific mission. The right system helps the company find rare people earlier, understand them more deeply, and move faster without lowering the bar.

Proposed next step
Align on the first three mission-critical hiring priorities, and build the first calibrated talent map together.
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