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IAIRO is seeking a Research Engineer to join our Alignment team. We are committed to advancing the frontier of Sovereign AI by developing compact, "right-sized" models that are as steerable and reliable as their massive counterparts. In this role, you will bridge the gap between base pretraining and real-world deployment. You wont just be fine-tuning checkpoints; you will be architecting the behavioral logic and safety frameworks for a new class of multimodal SLMs. Your work will focus on multi-domain alignment, ensuring our models can transition seamlessly between specialized fieldssuch as Legal, Healthcare, and Industrial Roboticswhile maintaining rigorous adherence to human intent and cultural values. Core ResponsibilitiesFrontier Alignment Research: Design and implement scalable alignment pipelines (SFT, DPO, PPO) to optimize 1B7B parameter models for high-stakes, domain-specific tasks. Advanced Preference Modeling: Architect reward models and preference datasets that capture nuanced domain expertise, moving beyond generic "helpfulness" to expert-level reasoning. Multi-Domain Synthesis: Develop innovative techniques to mitigate "alignment drift" and "catastrophic forgetting" when models are specialized across disparate industries (e.g., ensuring a model stays factually grounded in domain contexts while remaining flexible in creative ones). Evaluation & Red-Teaming: Devise rigorous, automated benchmarking suites (LLM-as-a-judge) and adversarial testing frameworks to validate model robustness in "out-of-distribution" scenarios. Open Source & Transparency: Contribute to the broader AI community by open-sourcing high-quality code and producing reproducible research that impacts the Sovereign AI ecosystem. Required Skills & ExperienceMasters 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). Proven Alignment Track Record: Significant experience with RLHF (Reinforcement Learning from Human Feedback), Direct Preference Optimization (DPO), and Constitutional AI. Scaling Knowledge: A deep understanding of Scaling Laws and the "Alignment Tax"knowing how to maximize performance in compute-constrained environments. Multimodal Familiarity: Experience aligning models that process not just text, but visual and sensor-based data. Bonus QualificationsPublication Record: Research results published at leading venues such as NeurIPS, ICML, ICLR, or MLSys. Synthetic Data Engineering: Experience building high-fidelity synthetic data pipelines to improve multi-step reasoning and logic. Hardware Awareness: Familiarity with optimizing inference engines (vLLM, TensorRT-LLM) or writing custom kernels (Triton/CUDA) for deployment on edge devices. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.