MathWorks, the developer of MATLAB and Simulink, is a leading global company in mathematical computing and engineering simulation. MathWorks OA evaluates candidates for roles like Software Engineer, Application Engineer, and Data Scientist by testing algorithmic thinking, mathematical modeling skills, and proficiency in MATLAB/Python. The OA emphasizes solving real-world engineering and computational problems efficiently.
MathWorks Typical OA Structure
The OA usually lasts 60–90 minutes and includes 2–3 programming/mathematical problems. The exact format varies by role:
- Software Engineer: Focus on data structures, algorithms, and software design.
- Application Engineer: Emphasizes MATLAB/Simulink modeling, numerical analysis, and engineering problem-solving.
- Data Scientist: Tests statistical analysis, machine learning, and data visualization.
High-Frequency Question Types & Sample Problems
Algorithmic Problem Solving
Focus: Sorting, searching, dynamic programming, or graph algorithms.
Sample Question: “Implement a function to find the shortest path in a weighted graph using Dijkstra’s algorithm. The input is an adjacency matrix, and the output is the shortest distance from the start node to all other nodes.”
Solution Approach:
Use a priority queue to select the node with the minimum distance iteratively. Update neighbors’ distances and track predecessors.
import heapq
def dijkstra(adj_matrix, start):
n = len(adj_matrix)
dist = [float('inf')] * n
dist[start] = 0
heap = [(0, start)]
while heap:
current_dist, u = heapq.heappop(heap)
if current_dist > dist[u]:
continue
for v in range(n):
weight = adj_matrix[u][v]
if weight > 0 and dist[v] > dist[u] + weight:
dist[v] = dist[u] + weight
heapq.heappush(heap, (dist[v], v))
return dist
MATLAB-Specific Modeling
Focus: Matrix operations, signal processing, or simulation (common in Application Engineer roles).
Sample Question: “Design a MATLAB script to filter out high-frequency noise from a signal using a low-pass Butterworth filter. The input is a noisy signal vector and the cutoff frequency; the output is the filtered signal.”
Solution Approach:
Use MATLAB’s butter and lfilter functions to design and apply the filter.
function filtered_signal = low_pass_filter(noisy_signal, cutoff_freq, fs)
% Design Butterworth low-pass filter
Wn = cutoff_freq / (fs/2); % Normalize cutoff frequency
[b, a] = butter(4, Wn, 'low');
% Apply filter
filtered_signal = lfilter(b, a, noisy_signal);
end
Mathematical Modeling & Optimization
Focus: Linear algebra, differential equations, or optimization algorithms.
Sample Question: “Solve a system of linear equations using Gaussian elimination. The input is a coefficient matrix and a constant vector; the output is the solution vector or ‘No solution’ if inconsistent.”
Solution Approach:
Implement row operations to reduce the augmented matrix to row-echelon form and check for consistency.
import numpy as np
def gaussian_elimination(A, b):
augmented = np.hstack((A, b.reshape(-1, 1)))
n = len(augmented)
for i in range(n):
# Find pivot
pivot = np.argmax(np.abs(augmented[i:, i])) + i
augmented[[i, pivot]] = augmented[[pivot, i]]
if augmented[i, i] == 0:
continue % No unique solution
# Normalize pivot row
augmented[i] /= augmented[i, i]
# Eliminate other rows
for j in range(n):
if j != i and augmented[j, i] != 0:
augmented[j] -= augmented[j, i] * augmented[i]
# Check for inconsistency
for row in augmented:
if np.all(row[:-1] == 0) and row[-1] != 0:
return "No solution"
return augmented[:, -1]
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