Deep Learning Engineer

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

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 Terrabase’s agentic systems — from perception to prediction, from insight to action.

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

https://forms.gle/zd3egMojvvqS9nQJ9

We review only complete submissions.

• 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.

• 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.

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

We’ll 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.

• 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.

We’re 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.