Description : 5+ years of Deep Learning Engineering role with strong end-to-end expertise in building and deploying machine-learning systems at scale.

Why This Role Matters :

Terrabase is building the next frontier of work AI an adaptive platform where ambient and specialized agents collaborate to deliver the one answer that matters instantly, safely, and with enterprise-grade precision.

Your mission : bring deep learning to life at scale. Architect, optimize, and operationalize intelligent models that power Terrabases agentic systems from perception to prediction, from insight to action.

Mandatory : To be considered for screening, please submit your details here :

We review only complete submissions.

What Youll Do :

- Build high-performance models : Design, train, and deploy deep neural networks (CNNs, RNNs, LSTMs, Transformers) for tasks across computer vision, NLP, and time-series forecasting.

- Engineer production pipelines : Implement MLOps workflows CI/CD, containerization, deployment, and monitoring to take models from notebook to production.

- Drive predictive intelligence : Apply time-series and anomaly-detection methods to power forecasting and early-warning systems.

- Marry ML with classical methods : Combine deep learning with boosting and ensemble models (XGBoost, LightGBM, CatBoost) to maximize accuracy and stability.

- Benchmark and tune : Run experiments, track results, and iteratively refine hyperparameters, architectures, and loss functions to achieve SOTA performance.

- Operationalize insight : Build reproducible pipelines leveraging data versioning, feature stores, and experiment-tracking tools (MLflow, DVC, W&B).

- Collaborate across teams : Partner with product and infra teams to ensure models meet business and latency targets, with strong monitoring and retraining loops.

- Document and share : Write crisp design notes, runbooks, and post-mortems to scale learning across teams.

What Were Looking For :

- Experience : 5+ years of hands-on work in machine learning or deep learning, with at least one deployed production system.

- Depth in Deep Learning : Strong foundations in CNNs, RNNs, LSTMs, and transformer-based architectures.

- Strong Python foundations : Proficient with modern ML/DL libraries such as PyTorch, TensorFlow, and Scikit-Learn.

- MLOps fluency : Familiar with Docker, Kubernetes, CI/CD, model deployment, and monitoring.

- Analytical rigor : Solid grounding in statistics, optimization, and time-series modeling.

- Practical problem-solver : Experience balancing performance, interpretability, and cost in real-world deployments.

- System thinking : You design not just models, but the data and feedback systems around them.

- Clear communicator : Able to reason from first principles and articulate trade-offs in writing and design reviews.

Engagement Type :

This will begin as a consulting / contract position, not a full-time role.

Well start with a one-month engagement, focused on specific deep learning deliverables, and may extend or convert it based on mutual fit, performance, and project alignment.

Bonus Points :

- Exposure to cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML).

- Familiarity with experiment-tracking and data-management tools (MLflow, DVC, Weights & Biases).

- Contributions to open-source ML frameworks or public technical blogs.

- Background in forecasting, anomaly detection, or operational intelligence systems.

Life at Terrabase :

Were a sharp, humble, fully-remote crew that values deep focus and fast feedback. Your work ships weekly to real customers supported by generous compute budgets and a culture that prizes clarity over ceremony.

Terrabase is an equal-opportunity employer. We celebrate diversity and are committed to building an inclusive environment for every team member.