Dandy·Machine Learning Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026Remote

Summary

Interviewed for an ML Engineer role at Dandy and got a geometry/math question that felt more like a graphics or robotics screen than anything ML-specific. Not what I was expecting.

Questions Asked (1)

Given two points in 3D space, write a function to return the linearly interpolated point at a parameter t between 0 and 1. Then discuss how you'd extend this to a polyline of N points, handle t values outside the valid range, and account for numerical precision issues.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The basic lerp part was fine, P(t) = (1-t)*P0 + t*P1, done in like two minutes.

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AI HintsAI Generated

Suggested Approach

Start by writing the simple linear interpolation function for two points, then systematically extend it to a polyline by mapping the global parameter t to the correct segment and local parameter. Discuss edge cases like t outside [0,1] (clamping vs. extrapolation) and numerical precision issues (floating-point errors, epsilon comparisons), emphasizing robustness and clarity.

Pro tip: Demonstrate awareness of real-world ML applications: interpolation is used in animation, trajectory prediction, and data augmentation, so mention how your implementation handles edge cases to avoid silent failures in production.

1. Implement basic linear interpolation

Write a function that takes two 3D points and a parameter t, returning the point (1-t)*P0 + t*P1. Use vector operations for clarity and efficiency.

2. Extend to polyline with N points

For a polyline of N points (N-1 segments), map the global t to a segment index and local t. If t is in [0,1], compute the scaled position along the total length or uniformly per segment, then interpolate within that segment.

3. Handle t outside [0,1]

Decide whether to clamp t to [0,1] (common for animation) or allow extrapolation (e.g., for prediction). Explain the trade-offs and implement accordingly, ensuring the function behaves predictably.

4. Address numerical precision

Use epsilon comparisons when checking t bounds or segment indices to avoid floating-point errors. Consider using double precision and stable formulas to minimize error accumulation.

5. Discuss trade-offs and alternatives

Mention alternative parameterizations (e.g., by arc length vs. uniform segments) and their impact on interpolation quality. Highlight when to use each based on application needs.

Key Points to Mention

Linear interpolation formula: P(t) = (1-t)*P0 + t*P1
Polyline interpolation: mapping global t to segment index and local t
Clamping vs. extrapolation for t outside [0,1] and when to use each
Numerical precision: epsilon comparisons, floating-point error, and stable computation
Parameterization by arc length vs. uniform segments for polyline
Edge cases: t exactly 0 or 1, degenerate segments (zero length), and N=1

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