Principal Research Engineer, Model Training & Post-Training

About the job

Responsibilities

• Own the model-improvement roadmap covering 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 for modern transformer-based and hybrid architectures, focusing on 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 encompassing 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 evaluations, quality gates, regression detection, release criteria, model-readiness reviews, and post-release monitoring.
• Partner with infrastructure and research engineering teams to enhance 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.

Requirements

• 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 regarding throughput, cost, reliability, observability, debugging, checkpointing, reproducibility, and fault tolerance.
• Excellent judgment concerning 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.

Benefits

• Meaningful equity component
• 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