Level

Inflection AI

Principal Research Engineer, Model Training & Post-Training

Inflection AI is hiring a Principal Research Engineer, Model Training & Post-Training in Palo Alto, United States. It pays $400k-$550k a year and Level rates it ; you can apply on Inflection AI.

Level's read on this job

  • Builds AI ●●●● is how Level rates this role: the job is building AI systems.
  • Tools and skills the posting cites include PyTorch, CUDA and Transformers.
  • Level classifies it as a lead role.
  • Level lists 4 other open roles at Inflection AI.

AI in this role

Lead model training, post-training, alignment, and evaluation strategies for Inflection AI's foundation models.

pytorchcudatransformers
ai-agentsfine-tuningmodel-trainingrlhfdpodistributed-trainingsynthetic-data

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

How we rate this

Principal Research Engineer, Model Training & Post-Training at Inflection AI rates 100 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agentsFine-tuningModel TrainingRlhfDpoDistributed TrainingSynthetic DataPyTorch

Questions you could be asked

  1. How do you decide when an AI agent can act on its own versus asking for approval first?
  2. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  3. Tell me about a project where model training was part of your work. What did you do?
  4. Tell me about a project where rlhf was part of your work. What did you do?
  5. Tell me about a project where dpo was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: AI agents, Fine-tuning, Model Training, Rlhf, and Dpo. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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