Data Scientist with AI and Optimization - Blend360 : Job Details

Job Description

We are looking for a Data Scientist to drive performance optimization for our clients AI Engine. This high-impact role will tackle complex computational bottlenecks and contribute to the re-engineering of simulation, training, and post-processing pipelines for large-scale industrial AI models. The candidate will collaborate with a multidisciplinary team to deliver quantifiable improvements in speed, scalability, and memory efficiency.

Responsibilities:

• Analyze and optimize AI engine code, focusing on removing performance bottlenecks in simulation, training, and post-processing workflows.

• Refactor sequential code and nested loops into efficient, vectorized operations, leveraging advanced knowledge in linear algebra and matrix decomposition.

• Diagnose and resolve computational inefficiencies related to GPU/TPU, non-vectorized data handling, and mixed framework operations (JAX, NumPy, Pandas).

• Develop and implement solutions for ODE (ordinary differential equation) solvers, optimization algorithms, and batch processing strategies.

• Lead root cause analysis for performance limitations and propose alternative algorithmic strategies (including MILP/LP, decomposition techniques, and alternative ODE solvers).

• Guide remediation and refactoring efforts, including memory optimization, JIT compilation, and data type standardization.

• Document improvements, monitor ongoing performance, and contribute to a roadmap for further scalability enhancements.

Qualifications: Qualifications

• Masters in Mathematics, Operations Research, Computer Science, or related quantitative field.

• Deep expertise in matrix decomposition, numerical optimization, and ODEs, with a demonstrated ability to apply these in real-world computation.

• Strong proficiency in Python, with hands-on experience in JAX, and experience with GPU/TPU acceleration.

• Prior exposure to optimization in scientific computing, especially in re-engineering or scaling data pipelines.

• Familiarity with ensemble methods, batch and vectorized computation, and memory management in large datasets.

• Ability to communicate technical findings and lead cross-functional discussions for codebase improvement.

Preferred:

• PhD preferred

• Background in large-scale industrial AI or simulation platforms.

• Knowledge of MILP, LP decomposition, and alternative ODE solvers (e.g., diffrax, odeint).

• Experience transitioning AI workloads from CPU to GPU/TPU environments.

• Experience with JAX, PyTorch or TensorFlow as alternative computation frameworks.

• Experience with CI/CD practices for ML/AI pipelines, profiling tools (e.g., cProfile, memory_profiler, jax.profiler), and performance benchmarking.