Research Engineer, Multi-Domain Alignment (SLM)

About Invyte, Inc
An AI-native hiring platform advancing Sovereign AI through compact, "right-sized" models that are steerable, reliable, and deployable across high-stakes domains.
Job Description
Bridge the gap between base pretraining and real-world deployment by architecting behavioral logic and safety frameworks for a new class of multimodal SLMs. Focus on multi-domain alignment to ensure models can transition seamlessly between specialized fields—Legal, Healthcare, and Industrial Robotics—while maintaining rigorous adherence to human intent and cultural values.
Key Responsibilities
Design and implement scalable alignment pipelines (SFT, DPO, PPO) to optimize 1B–7B parameter models for high-stakes, domain-specific tasks
Architect reward models and preference datasets that capture nuanced domain expertise, moving beyond generic helpfulness to expert-level reasoning
Develop innovative techniques to mitigate alignment drift and catastrophic forgetting when models are specialized across disparate industries
Devise rigorous, automated benchmarking suites (LLM-as-a-judge) and adversarial testing frameworks to validate model robustness in out-of-distribution scenarios
Contribute to the broader AI community by open-sourcing high-quality code and producing reproducible research
Qualifications
Master's or PhD in Computer Science, ML, or equivalent practical experience in training large-scale models
Expertise in Python and PyTorch, specifically within the Hugging Face ecosystem (Transformers, TRL, PEFT, Accelerate)
Significant experience with RLHF, Direct Preference Optimization (DPO), and Constitutional AI
Deep understanding of Scaling Laws and the Alignment Tax—maximizing performance in compute-constrained environments
Experience aligning models that process text, visual, and sensor-based data
Research results published at leading venues such as NeurIPS, ICML, ICLR, or MLSys (bonus)
Experience building high-fidelity synthetic data pipelines to improve multi-step reasoning and logic (bonus)
Familiarity with optimizing inference engines (vLLM, TensorRT-LLM) or writing custom kernels (Triton/CUDA) for edge deployment (bonus)

Work arrangement
No