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Role: Algorithm Developer
Location: BANGALORE
Experience: 3-8 Years
Employment Type: Full time
Notice Period:0-30 days(If you have negotiable notice period or buyout option please apply)
Requirements
Job Opening: Algorithm Developer / Engineer
Domain: Aerospace and
defence, multi-sensor data fusion
Openings
| Profile | Openings | Experience |
|---|
| Algorithm Developer,
MATLAB | 4 | 3 or more years |
| Algorithm Developer,
Python | 4 | 3 or more years |
| Lead Algorithm Engineer | 1 | 5 to 6 years |
Candidates strong in both MATLAB
and Python are preferred for every profile.
Common details
• Location: Bangalore, full-time at the client
site. Remote or hybrid work is not possible.
• Work environment: Closed network with no
internet access. You must derive equations and write code from your own
knowledge.
• Before joining: Police verification and
background check are mandatory.
Role summary
Design, develop and validate
data fusion, tracking and recognition algorithms. Prototype them in MATLAB or
Python, measure their performance, and hand the benchmarked algorithms to the
software team for embedded implementation.
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.
Must-have skills for every candidate
| 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 |
Algorithm areas
Candidates must have hands-on
experience in one or more of these areas. Experience in several is an
advantage.
| 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 | Task 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 the 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.
Benefits
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