<div class="content-intro"><h2><strong>About Inflection AI</strong></h2> <p>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.<br><br>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.</p></div><h2><strong>About the Role</strong></h2> <p>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.<br><br></p> <p><strong>What You’ll Do</strong></p> <ul> <li>Own the model-improvement roadmap across capability, reliability, emotional intelligence, tool use, safety, latency, cost, and enterprise readiness.</li> <li>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.</li> <li>Drive model architecture and optimization decisions across modern transformer-based and hybrid architectures, including both training-time and inference-time performance.</li> <li>Lead large-scale training efforts on distributed GPU clusters, including systems operating at the scale of 1,000+ GPUs.</li> <li>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.</li> <li>Build and improve evaluation and release-quality systems, including model evals, quality gates, regression detection, release criteria, model-readiness reviews, and post-release monitoring.</li> <li>Partner closely with infrastructure and research engineering teams to improve distributed training reliability, checkpointing, fault tolerance, observability, reproducibility, and cost-performance tradeoffs.</li> <li>Debug and improve model behavior across the full stack: data, training, post-training, evaluation, infrastructure, product integration, and production feedback.</li> </ul> <p><strong><br>What We’re Looking For</strong></p> <ul> <li>Experience leading, or serving as a principal contributor to, large-scale LLM, multimodal, or foundation-model training or post-training programs.</li> <li>Deep experience with transformer-based models, hybrid architectures, modern deep-learning frameworks, and distributed training systems.</li> <li>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.</li> <li>Experience operating or partnering on large-scale training infrastructure, ideally including GPU clusters at the scale of 1,000+ GPUs.</li> <li>Strong systems instincts around throughput, cost, reliability, observability, debugging, checkpointing, reproducibility, and fault tolerance.</li> <li>Excellent judgment around data quality, evaluation design, model regressions, release readiness, and production model behavior.</li> <li>Ability to balance research ambition with product pragmatism, user impact, and operational discipline.</li> <li>Experience leading senior technical teams while continuing to contribute directly to technical decisions and implementation.</li> <li>PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field, or equivalent practical experience.</li> </ul> <h2><strong>Employee Pay Disclosures</strong></h2> <p>At Inflection AI, we aim to attract and retain the best employees and compensate them in a way that appropriately and fairly values their individual contributions to the company. For this role, Inflection AI estimates a starting annual base salary to fall within the range of <strong>$400,000 to $550,000</strong>, depending on a candidate’s qualifications and level of experience. This role also includes a meaningful equity component, allowing employees to share in the long-term success of the company.<br><br></p> <h3><strong>Benefits</strong></h3> <p>Inflection AI values and supports our team’s mental, emotional, financial and physical health. We are focused on building a positive, safe, inclusive and inspiring place to work. Our benefits include: </p> <ul> <li>Robust medical, dental and vision options with employer contributions for HSA, FSA and DFSA</li> <li>401k matching program </li> <li>Flexible Time Off, 10 paid holidays, 5 days sick leave</li> <li>Parental, Medical and Family care leave </li> <li>Generous cell-phone, wellness and office set up stipends </li> <li>Support of country-specific visa needs for international employees living in the Bay Area</li> </ul>