Solve a challenging problem in your field to unlock application details
🔐 Prove Your Expertise
At Warpeed, we're building quantum-optimized materials for interstellar propulsion—one of humanity's most ambitious technical challenges.
We only hire the best. To apply, you must first demonstrate your technical expertise by solving a difficult problem in your field.
How it works: Select your role → Solve the technical challenge → Get the email address to send your CV
⚠️ Challenge Difficulty: These questions require real expertise in the field.
If you can't solve them, you're likely not qualified for the role. No hints or partial credit.
Select Your Target Position
Co-Founder & CTO
Equity: 15-25%
Chief Scientist
Equity: 5-10%
VP Business Dev
Equity: 3-7%
Materials Engineer
Equity: 1-3%
Quantum Engineer
Equity: 1-3%
Aerospace Engineer
Equity: 1-2%
Co-Founder & CTO Challenge
🔴 EXPERT LEVEL
A 100 m² lightsail (10m × 10m) with 4-layer dielectric stack is being accelerated by a 100 GW laser array at 1064 nm.
The sail has a reflectivity of 99.7% and experiences momentum transfer from photon reflection.
Calculate the instantaneous acceleration (in m/s²) of a 5 kg spacecraft+sail system at the moment of peak laser irradiance.
Provide your answer in the format: X.XX m/s²
Required knowledge: Radiation pressure physics, momentum transfer from photons, laser propulsion fundamentals.
Show understanding of P = 2RF/c for highly reflective surfaces.
What we're looking for: CTOs must deeply understand the physics of our propulsion system.
This problem tests radiation pressure calculation, a fundamental concept for lightsail design.
If you need to Google basic formulas, this role isn't for you.
Chief Scientist Challenge
🔴 EXPERT LEVEL
You're designing a quantum optimization algorithm using VQE (Variational Quantum Eigensolver) to find the ground state energy of a multi-layer dielectric material system.
The Hamiltonian has 8 qubits, and you're running on IBM Torino (133-qubit processor with error rates ~0.1% for 2-qubit gates).
Estimate the minimum number of measurements (shots) required per parameter update iteration to achieve 1% precision in energy estimation, assuming shot noise dominates and energy variance σ²ₑ ≈ 4.0 Ha².
Provide your answer as an integer: XXXXX shots
Required knowledge: Quantum computing statistics, shot noise, VQE optimization, precision requirements.
Use relationship: precision ∝ σ/√N where N is number of shots.
What we're looking for: Chief Scientists must master quantum algorithm optimization on real hardware.
This tests your understanding of shot noise, statistical precision, and practical VQE implementation—core to our quantum materials research.
VP Business Development Challenge
🟡 ADVANCED LEVEL
Warpeed has developed a quantum-optimized metamaterial for spacecraft thermal management that reduces radiator mass by 40% compared to traditional MLI (Multi-Layer Insulation).
You're targeting NASA's Gateway lunar station program, which has a $500M thermal systems budget over 5 years.
Design a 3-phase go-to-market strategy to capture $50M in contracts within 24 months. Include: (1) your entry point/beachhead, (2) key decision-makers you'd target, and (3) specific procurement vehicles (contracts, SBIRs, partnerships) you'd pursue.
Write a concise strategic plan (200-400 words).
Required knowledge: Aerospace contracting, NASA procurement processes, beachhead strategy, stakeholder mapping.
We're testing strategic thinking and domain expertise.
What we're looking for: VP BD must craft executable strategies for complex aerospace sales.
This tests your understanding of NASA's procurement, thermal systems market, and strategic B2G sales.
Generic answers will be rejected—we need specific, actionable plans.
Senior Materials Engineer Challenge
🔴 EXPERT LEVEL
You're depositing a 4-layer dielectric stack (TiO₂/SiO₂/HfO₂/Al₂O₃) using Ion Beam Sputtering (IBS) on a 25 μm polyimide substrate.
Layer thicknesses are: 120nm / 95nm / 140nm / 88nm. Target reflectivity at 1064 nm is >99.5%.
During deposition, your ellipsometer shows the TiO₂ layer is growing at 0.8 Å/s instead of the expected 1.2 Å/s.
What are the 3 most likely root causes, and what process parameter would you adjust first?
Format: List 3 causes, then state your first adjustment (e.g., "Causes: 1) X, 2) Y, 3) Z. Adjust: [specific parameter and direction]")
What we're looking for: Materials engineers must troubleshoot complex deposition processes in real-time.
This tests your hands-on experience with IBS, optical monitoring, and systematic problem-solving under tight specifications.
Quantum Computing Engineer Challenge
🔴 EXPERT LEVEL
You're implementing a QAOA (Quantum Approximate Optimization Algorithm) circuit to optimize layer thicknesses in a 6-layer dielectric stack.
The problem has 12 binary variables (2 qubits per layer for thickness discretization). You're using p=3 layers in QAOA.
How many parameterized rotation gates (RZ, RX, RY) will your ansatz circuit contain, assuming standard QAOA construction with ZZ interaction gates on a fully connected graph?
Provide your answer as: XXX gates
Required knowledge: QAOA circuit construction, parameterized quantum circuits, graph encoding, gate counting.
Count γ and β parameters across all p layers with proper mixer and problem Hamiltonian structure.
What we're looking for: Quantum engineers must design and scale optimization circuits efficiently.
This tests QAOA understanding, circuit construction, and ability to estimate resource requirements—critical for our SaaS platform.
Aerospace Systems Engineer Challenge
🔴 EXPERT LEVEL
A 6U CubeSat with deployed 10m × 10m lightsail (100 m² area) is in LEO at 550 km altitude.
Initial orbit: circular, i=53°. After sail deployment, atmospheric drag force is 2.8×10⁻⁴ N. Spacecraft+sail mass: 8 kg.
Calculate the approximate time (in days) until orbital altitude decays to 400 km, assuming constant atmospheric density at initial altitude and circular orbit approximation.
Provide answer as: XX.X days
Required knowledge: Orbital mechanics, drag effects, altitude decay modeling, atmospheric density models, 2-body problem dynamics.
Use energy decay method with constant drag approximation.
What we're looking for: Aerospace engineers must accurately model orbital dynamics for mission planning.
This tests orbital decay analysis, drag modeling, and practical systems engineering for lightsail deployment scenarios.