Posted on: 24/11/2025
Description :
- Own technical charters and roadmap for multiple ML/CV initiatives.
- Lead and mentor applied scientists, mapping complex EO problems into actionable, scalable technical solutions.
- Drive hypothesis generation, experimentation, architecture design, model development, and deployment for production ML pipelines.
- Own E2E delivery of large-scale ML/CV systems from problem framing to data design, model development, deployment, and monitoring.
- Collaborate with Product, MLOps, Platform, and Geospatial experts to convert ambiguous requirements into elegant solutions.
- Communicate technical findings to leadership, customers, and cross-functional partners with clarity and precision.
- Assist data science managers in effective project, resource management, and timely deliverables (in an agile manner), via showcasing strong sense of ownership and accountability.
- Build reliable, efficient models that scale across geographies, seasons, sensors, and business domains.
- Write clean, scalable production-grade code in Python/PyTorch.
- Conduct A/B experiments and calibrate ML metrics to business KPIs.
- Innovate on model architectures (Transformers, diffusion, generative, time-series models, self-supervision, multimodal fusion and temporal modeling) to advance in-house geospatial ML SOTA.
- Represent your work through patents, technical documents, internal whitepapers, and publications (as applicable).
- Contribute to hiring and technical excellence, including mentoring junior team members and interns.
Experience :
- 2+ years in a technical leadership role people and project leadership.
- Proven experience taking ML models from POC production monitoring.
Must Have Technical Expertise :
- A proven track record of relevant experience in computer vision, NLP, learning theory, optimization, ML+Systems, foundational models, etc.
- Technically familiar with some, or most of (as evidenced by problem solving skills in novel scenarios) : Transformers, UNet, RNNs/LSTMs/GRUs, YOLO/RCNN/EncoderDecoder architectures, Generative models (GAN, VAE, Diffusion), Self-supervised & contrastive learning, Representation learning, domain adaptation & generalization, Semi-/Active learning, noisy-label learning, Super-resolution, anomaly detection, clustering, Model compression: distillation, pruning, quantization.
- PyTorch, Python, SQL, distributed systems (Spark), MLOps for large-scale training, data pipelines, and deployment.
Bonus :
- First-authored publications in ICLR, NeurIPS, CVPR, ICCV, ECCV, ICML, AAAI, IGARSS, IEEE TGRS, etc.
- Experience with geospatial datasets, climate models, foundation models, or EO analytics.
Benefits :
- Access to mental health experts for you and your family.
- Dedicated allowances for learning and skill development.
- Comprehensive leave policy with casual leaves, paid leaves, marriage leaves, bereavement leaves.
- Twice a year appraisal.
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