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Inflection AI

engineering opportunity

Principal Research Engineer, Model Training & Post-Training

Lead the end-to-end model improvement loop, encompassing data strategy, training, post-training, and release criteria. Drive the technical roadmap for model capabilities, reliability, and emotional intelligence while managing large-scale GPU clusters.

Palo Alto, California, United StatesonsiteFULL_TIME

Posted

About the role

What will you do at Inflection AI?

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.
  • EMPLOYEE PAY DISCLOSURES
  • 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 $400,000 to $550,000, 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.
  • BENEFITS
  • 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:
  • * Robust medical, dental and vision options with employer contributions for
  • HSA, FSA and DFSA
  • * 401k matching program
  • * Flexible Time Off, 10 paid holidays, 5 days sick leave
  • * Parental, Medical and Family care leave
  • * Generous cell-phone, wellness and office set up stipends
  • * Support of country-specific visa needs for international employees living in
  • the Bay Area

Which skills does this role require?

Large Language ModelsModel TrainingPost-TrainingRLHFDPOSFTDistributed TrainingTransformer ArchitecturesData CurationModel EvaluationGPU Cluster ManagementReward ModelingSynthetic DataPreference OptimizationDeep Learning FrameworksTechnical LeadershipLLMMultimodalFoundation ModelsGRPORLAIFTool-use Fine-tuningDistillationTransformerHybrid ArchitecturesGPU ClustersData DecontaminationHuman-in-the-loopModel EvalsRegression DetectionFault ToleranceObservabilityCheckpointingDeep LearningAI AgentEmotional IntelligencePalo AltoInflection AIPiMachine LearningLLMsProduct Strategy

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