At Esper, we are building the sensor infrastructure to measure the physical world. Our hyperspectral imaging systems capture data beyond the visible spectrum, providing critical insights for mineral exploration and environmental monitoring. As we scale our infrastructure and push for stronger outcomes from our sensing stack, we're hiring an AI Engineer to join our team and help us move faster.

Demand in the critical minerals space has grown quickly in recent years, and we want our users to get fast, accurate answers.

We're not starting from zero: our core system is already in active development, so you'll be building on real infrastructure from day one, working side by side with the team who designed and built it. You'll have room to help shape and extend it further, rather than sitting downstream of a larger team. Expect fast iteration and direct ownership, hands-on from the start.

Responsibilities

- Fine-tune and adapt pretrained foundation models for domain-specific tasks on multi-dimensional imaging data
- Work with the analytics team to translate their domain knowledge and requirements into concrete ML objectives
- Build robust training and inference pipelines
- Design experiments with measurable metrics, run ablation studies, and conduct failure analysis
- Support work on RAG pipelines that inform model behavior as well as power analyst-facing querying
- Collaborate directly on architecture and modeling decisions

Must-have qualifications

- 3+ years of qualified or equivalent hands-on experience building and training deep learning models
- Proficiency in Python and PyTorch
- Strong fundamentals in deep learning and computer vision
- Experience training and fine-tuning pretrained or foundation models
- Experience with embedding models, including extraction and downstream use
- Ability to design experiments, define measurable metrics, run ablation studies, and conduct failure analysis
- Experience working with multi-dimensional image data




- Ability to reproduce and extend research papers
- Understanding of transformer and attention-based vision architectures
- Experience with embeddings, vector search, or retrieval systems that feed into model behavior. Project-level experience counts.
- Strong communication skills, with the ability to clearly document experiments, findings, and technical decisions

Nice-to-have qualifications

- Degree/coursework in computer science, machine learning, or a related quantitative field, or equivalent practical experience
- Publications or contributions in deep learning, computer vision, geoscience, remote sensing or related venues (conferences, workshops, arXiv)
- Familiarity with structured scientific or sensor data in signal-heavy, high-dimensional domains
- Experience with a vector database, such as FAISS, Pinecone, or Qdrant
- Experience with self-supervised pretraining or frozen-backbone fine-tuning
- Exposure to pretraining models from scratch, including data curation, self-supervised objectives, and training at scale, even at small or academic scale
- Any other skills that could benefit a small, cross-functional team even outside deep learning, we'd love to hear about them

Room to grow

If existing foundation models don't meet our needs, this role could extend into ground-up model pretraining, covering architecture design, self-supervised objectives, and training infrastructure. This isn't a day-one deliverable. It's a direction we're seriously evaluating, and candidates excited by that possibility will stand out.

Why Esper?

You'll be a foundational member of a fast-moving team, with direct impact on the quality of data we deliver to the world. We're a flat team, i.e., short paths from idea to decision, and your input shapes the product from the very first day. We value high-agency people who collaborate well and push themselves to build a better product.

Ready to build the intelligence powering our next-generation hyperspectral systems?

📌 AI Engineer (Australia)
🏢 Esper Satellite Imagery
📍 Australia