Principal Research Engineer, Model Training & Post-Training

About Inflection AI Inflection AI is a Public Benefit Corporation empowering people with human-centered, emotionally intelligent AI. We’re shaping the future of AI by combining emotional intelligence (EQ) and raw intelligence (IQ) to elevate people’s potential. Inflection AI created Pi, the world’s first emotionally intelligent AI, to help people work through decisions, emotions, and challenges.

Pi is a personal AI agent powered by Inflection AI’s foundation model, proving that AI can be personal, empathetic, and contextually aware.

About The Role

Inflection’s models are central to our product and platform strategy, and we are looking for a hands-on technical leader to own the model-improvement loop from data and training through evals, post-training, release criteria, and production feedback. This person will sit at the intersection of research, production engineering, and model release, with a mandate to ship models that are measurably better for users. The ideal candidate has led serious model training or post-training work before, can make principled tradeoffs across data, compute, architecture, and quality, around a clear technical roadmap.

What You’Ll Do

Own the model-improvement roadmap across capability, reliability, emotional intelligence, tool use, safety, latency, cost, and enterprise readiness. Lead training and post-training strategy, including supervised fine-tuning, RLHF, DPO, GRPO, RLAIF, reward modeling, preference optimization, tool-use fine-tuning, distillation, synthetic data, and related methods. Drive model architecture and optimization decisions across modern transformer-based and hybrid architectures, including both training-time and inference-time performance.

Lead large-scale training efforts on distributed GPU clusters, including systems operating at the scale of 1,000+ GPUs. Define and execute data strategy across data curation, mixture design, deduplication, decontamination, human-in-the-loop pipelines, preference data, evaluation data, synthetic data, and production feedback loops. Build and improve evaluation and release-quality systems, including model evals, quality gates, regression detection, release criteria, model-readiness reviews, and post-release monitoring.

Partner closely with infrastructure and research engineering teams to improve distributed training reliability, checkpointing, fault tolerance, observability, reproducibility, and cost-performance tradeoffs. Debug and improve model behavior across the full stack: data, training, post-training, evaluation, infrastructure, product integration, and production feedback.

What We’Re Looking For

Experience leading, or serving as a principal contributor to, large-scale LLM, multimodal, or foundation-model training or post-training programs. Deep experience with transformer-based models, hybrid architectures, modern deep-learning frameworks, and distributed training systems. Strong practical experience with post-training and alignment methods such as SFT, RLHF, DPO, GRPO, RLAIF, reward modeling, preference optimization, tool-use fine-tuning, or related approaches.

Experience operating or partnering on large-scale training infrastructure, ideally including GPU clusters at the scale of 1,000+ GPUs. Strong systems instincts around throughput, cost, reliability, observability, debugging, checkpointing, reproducibility, and fault tolerance. Excellent judgment around data quality, evaluation design, model regressions, release readiness, and production model behavior.

Ability to balance research ambition with product pragmatism, user impact, and operational discipline. Experience leading senior technical teams while continuing to contribute directly to technical decisions and implementation. PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field, or equivalent practical experience.