Principal Research Engineer, Model Training & Post-Training

Inflection AI

  • Palo Alto, CA
  • 30+ days ago
  • $400,000–$550,000 Per Year
  • Autofill and Review
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Skills

  • Artificial Intelligence (AI)unmatched
  • Computer Architectureunmatched
  • Computer Scienceunmatched
  • Data Analysisunmatched
  • Data Qualityunmatched
  • Debugging Skillsunmatched
  • Deep Learningunmatched
  • Distributed Computingunmatched
  • Employee Retentionunmatched
  • Flexible Spending Accountsunmatched
  • GPU (Graphics Processing Unit)unmatched
  • Leadershipunmatched
  • Machine Learningunmatched
  • Mail Processingunmatched
  • Model Reviewunmatched
  • Product Strategyunmatched
  • Productivity Managementunmatched
  • Reliability Engineeringunmatched
  • Research Skillsunmatched
  • System Operationsunmatched
  • Team Buildingunmatched
  • Team Lead/Managerunmatched
  • Technical Leadershipunmatched
  • Training Programunmatched

Description

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

Numbers & Facts

LocationPalo Alto, CA
Salary$400,000–$550,000 Per Year

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