Research Engineer – Pretraining

Prime Intellect

. Build and extend the pretraining code in prime-rl, taking small experiments into full training runs .

Posted 10/16/2026full-timeRemote • California • United StatesMid-LevelSenior💰 $150,000 - $350,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in training language models with a strong focus on Python and PyTorch, alongside a practical understanding of model architectures and optimization techniques. Capable of designing experiments that yield reproducible results while ensuring system resilience and efficiency.

Highest-signal resume keywords

Python ProgrammingPyTorch FrameworkLanguage Model TrainingModel Architecture OptimizationDistributed Training Debugging

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Training Code ModificationModel EvaluationCheckpointing and RecoveryNumerical Stability AnalysisControlled Ablations

Soft Skills

Good JudgmentIndependent Investigation

Industry Keywords

MoEAttention DesignsTraining DynamicsEnd-to-End ThroughputResearch Questions

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Build and extend the pretraining code in prime-rl, taking small experiments into full training runs
Evaluate model architecture changes, including MoE and attention designs, against model quality and training cost
Profile training jobs and improve end-to-end throughput with systems engineers on hardware-aware execution
Make long runs resilient to failures through reliable checkpointing and recovery
Investigate training dynamics and numerical stability, ensuring optimizations preserve convergence
Run controlled ablations to guide larger experiments while keeping setup and results reproducible
Work with data and evaluation researchers on training mixtures, including midtraining and long-context experiments
Take the next open models from architecture experiments through full pretraining runs, scaling toward 1–3T parameters

Requirements

What you’ll need

Hands-on experience training language models, including modifying training code and following the effects through actual runs
Strong Python skills
Experience with PyTorch or a comparable framework
Systems understanding sufficient to debug distributed training
Practical understanding of model architectures and optimization, including their effect on convergence
Ability to design ablations that answer research questions and explain what results do and do not show
Good judgment about what to investigate independently and when to involve a colleague

Benefits

Comp & perks

Equity incentives included in cash compensation range
Flexible work arrangements, with the option to work remotely or in-person at offices in San Francisco
Visa sponsorship
Relocation assistance for international candidates
Quarterly team off-sites
Hackathons
Conferences and learning opportunities
Opportunity to work with a talented, hard-working and mission-driven team