Algorithm Developer/Engineer

Open Positions : 9
Experience: 3+ years to 6 years.
Domain: Aerospace and defence, multi-sensor data fusion
Working hours: 9 am to 6pm. (No remote/ hybrid)
Location: Bangalore (client site)
Work environment: Closed network with no internet access.
Apply: hr@aiila.com
Job Description:
Key responsibilities
Convert mathematical equations and algorithm descriptions into working code.
Design and implement fusion, tracking or recognition algorithms.
Build sensor models from interface specifications.
Build simulations and generate test data to check the algorithms.
Measure performance with numbers: accuracy, track continuity, false track rate and latency.
Benchmark candidate algorithms and recommend the best performer.
Define test cases and carry out standalone algorithm testing.
Write clear, commented code and document the equations, design and results.
Present designs at formal design reviews and close the action points raised.
Support the software team when the algorithm is converted to C/C++.
Qualification
B.E./B.Tech in ECE, EEE, CSE or Aerospace, or M.Sc in Mathematics or Physics. M.E./M.Tech is preferred.
Minimum 3 years of hands-on algorithm development in MATLAB or Python.
Area What you must be able to do
MATLAB programming (MATLAB profile): Scripts and functions, matrix indexing, vectorised code, structures and cell arrays, plotting, file handling and debugging. Write algorithms with core MATLAB functions, without depending on toolboxes
Python programming (Python profile): Data types, functions, classes, lists and dictionaries, file and exception handling, and debugging. NumPy arrays, slicing and broadcasting, SciPy linear algebra and Matplotlib
Matrix mathematics: Multiplication, transpose, inverse, determinant and rank. Eigenvalues and eigenvectors. Solving linear equations. Positive-definite matrices. Cholesky, QR and SVD decompositions
Probability and statistics: Random variables, mean, variance, covariance and correlation. Gaussian distribution. Bayes rule. Generating and analysing noise
Estimation theory: Least squares, and the Kalman filter family (KF, EKF, UKF): predict step, update step, process noise and measurement noise
Coordinate frames: Polar, spherical and Cartesian conversion. NED and ECEF frames. 2D and 3D rotation matrices
Numerical methods: Interpolation, numerical integration and differentiation, root finding, and awareness of precision errors
Equations to code: Read a derivation, implement it correctly, and check the output against a hand calculation
C basics: Data types, loops, functions, arrays, pointers and structures. Read and write simple C programs
Working independently: Write code without internet search or AI tools, recalling common library functions and syntax from memory
Area Algorithms and skills
State estimation and tracking: EKF, UKF, IMM filters, motion models (constant velocity, constant acceleration, coordinated turn), noise and bias modelling, track-to-track fusion, covariance intersection
Data association and track management: Gating, GNN, JPDA, MHT, assignment algorithms (Hungarian, auction), track initiation, confirmation and deletion, duplicate track merging
Sensor resource management: ask scheduling, rule-based priority logic, optimisation, information-gain based sensor tasking
Target detection and recognition (Python required): OpenCV, PyTorch or TensorFlow, CNN and transformer-based object detectors, IR and EO image processing, video object tracking, dataset preparation and augmentation, model export to ONNX
Networked and multi-platform fusion: Triangulation, passive ranging, bearings-only tracking, geolocation, ambiguity resolution, time alignment of data from different platforms
Additional requirements for Lead Algorithm Engineer
The lead is the single technical lead for the full project team and the point of contact with the client.
5 to 6 years of algorithm development experience.
Hands-on radar tracking experience covering the full track life cycle: initiation, confirmation, maintenance and deletion.
Working knowledge of data association, Kalman and multiple-model filters, and multi-sensor fusion.
Strong in both MATLAB and Python, able to review the code and mathematics of other engineers.
Experience guiding a team, planning tasks, tracking progress and reporting to a client.
Able to present and defend technical designs at formal reviews.
Tech Stack:
Languages: MATLAB/Simulink or Python, and basic C/C++.
MATLAB toolboxes: Sensor Fusion and Tracking, Statistics and Machine Learning, Signal Processing, Optimization, Computer Vision.
Python libraries: NumPy, SciPy, FilterPy, Stone Soup, Pandas, Matplotlib, OpenCV, PyTorch, ONNX.
Version control: Git.
Documentation: MS Word, Visio or draw.io.
Good to have:
Experience with airborne or defence sensor data: radar, IRST or EO/IR.
Monte Carlo simulation and performance evaluation (RMSE, OSPA, NEES/NIS).
Experience in navigation (GPS/INS), ADAS sensor fusion or surveillance systems.
C/C++ experience, for easier handover to embedded implementation.
Published papers or a thesis in tracking, estimation, signal processing or computer vision.

Work arrangement
No