Junior Applied ML / sLLM Developer

SIGA is looking for a hands-on Junior Applied ML / sLLM Developer to join its algorithms and research team.

The position focuses on developing and optimizing small language models (sLLMs) and machine learning components that operate in air-gapped, offline OT environments. The role combines applied research with practical implementation, targeting secure, efficient, and explainable AI capabilities for cybersecurity and decision-support use cases.

The role is suitable for outstanding MSc or PhD graduates.

Responsibilities:
Develop and implement offline ML and sLLM-based components in Python.
Run, evaluate, and optimize small language models for local inference (CPU / limited GPU).
Apply model compression techniques such as quantization, LoRA, pruning, or distillation.
Build and maintain offline RAG-style pipelines using local data sources.
Research and prototype ML / LLM approaches relevant to cybersecurity and OT environments.
Run experiments, simulations, and back-testing on real and simulated data.
Participate in the design of secure, deterministic, and explainable AI pipelines.
Collaborate with research and engineering teams to move algorithms toward production.
Generate research and specifications documents.
Requirements:

MUST

Sc. in Computer Science / Electrical Engineering / Computer Engineering or equivalent.
Strong Python programming skills and solid software engineering background.
MSc graduate (or near completion) or early-stage PhD candidate.
Strong foundations in machine learning and deep learning.
Experience implementing and evaluating ML models.
Strong analytical thinking and problem-solving skills.

Advantage

Experience with LLMs or transformer-based models.
Familiarity with model optimization or compression techniques.
Hands-on experience with PyTorch and/or TensorFlow.
Experience with time-series data or anomaly detection.
Interest or background in cybersecurity, OT, or industrial systems.
Experience working in constrained, offline, or edge environments.

Candidates who do not meet the required qualifications will not be considered.