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Zipline·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
May 2026

Summary

Technical review round at Zipline for a software engineering role, focused entirely on embedded systems and signal processing. Two meaty problems back to back, both requiring you to think about real constraints rather than just algorithmic correctness. Left feeling like I probably undersold some of my embedded experience.

Questions Asked (2)

Q1

You're building a real-time 2.5D elevation mapping system on an embedded computer. Given timestamped 3D sensor points and vehicle poses, how would you design the data structures, update logic, and optimizations to meet CPU, memory, and latency constraints?

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

This is where I spent most of my mental energy.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the constraints (sensor rate, map resolution, latency budget) and then propose a 2.5D grid-based representation with efficient update logic. Focus on incremental updates, spatial indexing, and memory optimizations to meet real-time performance on embedded hardware.

Pro tip: Emphasize the trade-off between map resolution and computational load, and mention using fixed-point arithmetic and SIMD where available to speed up updates on embedded CPUs.

1. Clarify Requirements and Constraints

Ask about sensor data rate, map resolution, latency and memory limits, and vehicle dynamics to scope the problem. This ensures your design targets the right bottlenecks.

2. Choose Data Structures

Propose a 2.5D grid (e.g., 2D array of height values) with optional multi-resolution or tiled layout. Use a spatial index like a quadtree or hash map for efficient point-to-cell mapping.

3. Design Update Logic

Process points incrementally: transform points to world frame using poses, then update affected cells with a weighted average or Kalman filter. Only update cells within sensor range to avoid unnecessary work.

4. Optimize for Embedded Constraints

Apply memory pooling, fixed-point arithmetic, and parallelization (e.g., SIMD or multi-threading) where possible. Use lazy evaluation or level-of-detail to reduce CPU load.

5. Validate and Iterate

Discuss how to profile and benchmark the system, and how to adjust parameters (e.g., resolution, update rate) to meet latency and memory budgets.

Key Points to Mention

  • 2.5D elevation map representation (height grid) vs full 3D
  • Incremental updates with spatial indexing (e.g., quadtree, hash map)
  • Sensor fusion and pose transformation (timestamped points and vehicle poses)
  • Memory optimization: tiling, compression, fixed-point arithmetic
  • CPU optimization: SIMD, multi-threading, lazy evaluation
  • Latency constraints: real-time processing, bounded update times

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

There's a causal 1D filter in the codebase used to smooth a noisy sensor signal. Without seeing the code, what correctness, robustness, and testing issues would you look for, and how would you address them?

Technical Trade-offsRoot Cause AnalysisSystem Design
Author's notes

Honestly a weird question to get verbally since you're reviewing code you can't see.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the filter's purpose and constraints, then systematically evaluate correctness (e.g., algorithm, boundary handling), robustness (e.g., noise, missing data), and testing (e.g., unit, property-based). Propose concrete improvements and tests, emphasizing trade-offs and real-world sensor conditions.

Pro tip: Demonstrate awareness of real-world sensor issues like outliers and dropouts, and suggest property-based testing to catch edge cases. Mention that you'd first reproduce the issue with a minimal test case to isolate the filter's behavior.

1. Clarify requirements and context

Ask about the filter's purpose, expected signal characteristics, latency constraints, and how it's used in the system. This ensures you focus on relevant issues.

2. Assess correctness

Review the filter algorithm for mathematical correctness, boundary handling (e.g., initial conditions), and numerical stability. Check for off-by-one errors and proper normalization.

3. Evaluate robustness

Consider how the filter handles noisy data, outliers, missing samples, and varying sample rates. Look for potential overflow, underflow, or division by zero.

4. Examine testing strategy

Check for unit tests covering edge cases, property-based tests for invariants, and integration tests with realistic sensor data. Suggest adding tests for boundary conditions and fault injection.

5. Propose improvements and trade-offs

Recommend specific fixes (e.g., clamping, outlier rejection) and discuss trade-offs between smoothing, latency, and complexity. Prioritize based on impact.

Key Points to Mention

  • Boundary conditions: initial state, first/last samples, and handling of incomplete windows.
  • Numerical stability: potential overflow/underflow, precision loss, and use of appropriate data types.
  • Outlier and missing data handling: strategies like median filtering, interpolation, or flagging invalid samples.
  • Real-time constraints: latency introduced by the filter and its impact on control loops.
  • Testing: unit tests for known inputs, property-based tests for invariants (e.g., output bounds), and fuzzing with noisy data.
  • Code quality: readability, modularity, and documentation of assumptions and limitations.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.