About Gradera
Gradera defines a new category of enterprise transformation called Software-Orchestrated Services™ - where software orchestrates human expertise, digital workers, and enterprise systems to deliver governed outcomes at scale. As an AI Native Services firm, we help enterprises redesign how work gets done across operations, product, engineering, customer experience, data, and enterprise workflows to move beyond fragmented AI pilots and disconnected automation toward measurable business outcomes.
Overview
We are seeking skilled ML Engineers to join our Simulation & Scenario Enablement team. This is a specialized role at the intersection of machine learning engineering and physics-based simulation. You will design and implement production-grade ML pipelines, build physics-informed neural networks (PINNs) that respect physical constraints, and develop neural architectures that accelerate simulation workloads. You will own the full MLOps lifecycle — from feature engineering and model training to deployment, monitoring, and continuous improvement — ensuring ML models reliably power real-time scenario evaluation and digital twin intelligence.
Our core ML engineering stack includes:
ML Frameworks & Development
PyTorch and TensorFlow for neural network development
Physics-Informed Neural Networks (PINNs) for constraint-aware modeling
Neural ODE solvers (torchdiffeq, diffrax) for continuous-time dynamics
Python (NumPy, SciPy, pandas) for numerical computing
MLOps & Platform
Databricks ML for scalable model training and pipelines
MLflow for experiment tracking, model registry, and deployment
Unity Catalog for ML asset governance and lineage
Delta Lake for feature storage and versioned training data
Feature Store for feature management and serving
Production & Monitoring
Model serving and inference optimization
Model monitoring, drift detection, and alerting
CI/CD for ML pipelines
Containerized model deployment (Docker, Kubernetes/OpenShift)
Key Responsibilities
Design and implement Physics-Informed Neural Networks (PINNs) with domain constraints
Develop neural ODE solvers and surrogate models for physics simulations
Build hybrid ML architectures combining data-driven learning with physics-based priors
Optimize neural models for accuracy, inference speed, and resource efficiency
Design scalable feature engineering pipelines using Databricks and PySpark
Manage features in Feature Store and build Delta Lake training pipelines
Build end-to-end ML pipelines on Databricks ML
Track experiments, version models, and deploy using MLflow
Implement model monitoring for drift, performance, and prediction quality
Build CI/CD for ML and ensure governance via Unity Catalog
Preferred Qualifications
7+ years of experience in ML engineering, applied ML, or scientific computing roles
Master’s or PhD in Computer Science, Machine Learning, Computational Science, Physics, or related field
Track record of deploying ML models in production at scale
Experience with physics-based or scientific ML applications
Experience working in agile, cross-functional teams
Highly Desirable
Experience with ML for digital twin or simulation platforms
Background in computational physics , numerical methods , or scientific computing
Experience with differentiable programming and automatic differentiation frameworks
Familiarity with discrete event simulation or agent-based modeling integration
Experience with GPU-accelerated training and inference optimization
Publications or patents in physics-informed ML, neural ODEs, or surrogate modeling
Contributions to open-source ML/scientific computing projects
Exposure to industrial domains such as Manufacturing, Logistics, or Transportation is a plus