---
title: 'Machine Learning Engineer — Reinforcement Learning at Institute of Foundation Models'
canonical: 'https://feeny.ai/job/machine-learning-engineer-reinforcement-learning-institute-of-foundation-models-tyerejn1ya2d'
type: 'job'
last_seen: '2026-10-19'
---

# Machine Learning Engineer — Reinforcement Learning at Institute of Foundation Models

- **Company:** Institute of Foundation Models
- **Location:** Sunnyvale, CA
- **Compensation:** $150k–$450k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-05
- **Last confirmed live:** 2026-10-19
- **Apply:** https://jobs.lever.co/ifm-us/6ef8a25f-786b-4362-a947-b9c1d2878e2b

## Job description

## About the Institute of Foundation Models

We are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy.

As part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub for high-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AI pioneers.

## Key Responsibilities

- Distributed Framework Ownership – Extend or modify training frameworks (e.g., DeepSpeed, FSDP) to support new use cases and architectures.
- Training & Inference Integration – Connect training workers with rollout generation engines (e.g., vLLM, SGLang), including trajectory exchange and policy-weight synchronization.
- Launch Config & Debugging – Create and debug multi-node launch scripts with flexible batch sizes, parallelism strategies, and hardware targets.
- Metrics & Monitoring – Build systems for experiment tracking, job monitoring, and logging usable by collaborators and researchers.
- Infra Engineering – Write production-quality code and tests for ML infra in PyTorch or JAX; ensure reliability and maintainability at scale.
- RL Pipeline Development – Implement reward/verifier integration and trajectory processing, and validate log probabilities, token masks, and loss inputs with researchers.
- RL Execution & Recovery – Coordinate rollout and training workers, including checkpoint/restart and failure recovery; track policy versions and sample staleness when using asynchronous execution.

## Qualifications

Must-Haves:

- 5+ years of experience in ML systems, infra, or distributed training
- Experience modifying distributed ML frameworks (e.g., DeepSpeed, FSDP, FairScale, Horovod)
- Strong software engineering fundamentals (Python, systems design, testing)
- Proven multi-node experience (e.g., Slurm, Kubernetes, Ray) and debugging skills (e.g., NCCL/GLOO)
- Ability to implement algorithms across GPUs/nodes based on mathematical specs
- Experience working on an ML platform/ infrastructure, and/or distributed inference optimization team
- Experience with large-scale machine learning workloads (strong ML fundamentals)
- Hands-on experience developing an LLM RL pipeline, with ownership across rollout/inference and distributed training integration.
- Working knowledge of policy optimization methods such as PPO or GRPO, sufficient to implement and debug sampling, log probabilities, and policy updates.

Nice-to-Haves:

- Exposure to mixed-precision training (e.g., bf16, fp8) with accuracy validation
- Familiarity with performance profiling, kernel fusion, or memory optimization
- Open-source contributions or published research (MLSys, ICML, NeurIPS)
- CUDA or Triton kernel experience
- Experience with large-scale pre-training
- Experience building custom training pipelines at scale and modifying them for custom needs
- Deep familiarity with training infrastructure and performance tuning
- Experience with asynchronous RL, multi-turn or tool-using rollout environments, or custom reward/verifier systems.

## Benefits Include

*Comprehensive medical, dental, and vision benefits *Bonus *401K Plan *Generous paid time off, sick leave and holidays *Paid Parental Leave *Employee Assistance Program *Life insurance and disability

## About Institute of Foundation Models

## Company Overview
- **One-liner**: The Institute of Foundation Models (IFM) is a global AI research lab dedicated to the open and independent development of frontier-class foundation models, operating under the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).
- **Entity Type**: Academic Research Institute (part of MBZUAI, a university)
- **Headquarters**: Abu Dhabi, United Arab Emirates (with labs in Sunnyvale, CA, USA and Paris, France)
- **Founded**: 2026 (launched with Silicon Valley lab)
- **Founders**: Part of MBZUAI, led by President Eric Xing

## Core Business
- **Primary industry**: Artificial Intelligence research, foundation model development, open-source AI
- **Target customers**: Researchers, academia, industry partners, and the global AI community (B2B/Research/Public sector)
- **Mission**: To produce the world’s leading models across modalities while ensuring responsible, open, and socially meaningful impact.

## Products & Services
For each major offering:
- **JAIS Series**: State-of-the-art open-source Arabic LLM covering Modern Standard Arabic and regional dialects, including Moroccan Arabic (Darija). Fully documented training lifecycle.
- **Vicuna**: Lightweight open chatbot surpassing 90% of ChatGPT and Bard quality.
- **K2 (K2-65B)**: Large language model with advanced reasoning capabilities, focusing on sustainable performance. Updated version soon to be released.
- **PAN World Model**: Next-gen foundation model for embodied reasoning and physical-world simulation, integrating multimodal inputs (language, video, spatial data, physical actions). Includes PAN-Agent for multimodal reasoning tasks.
- **FM For Bio – GET**: Domain-specific model for biology.
- **LLM360**: Open-source framework enhancing transparency and collaboration in LLM research, providing training code, datasets, and model checkpoints.
- **Research partnerships**: Collaboration with academia, startups, and enterprises via shared compute, co-publishing, and co-development.

## Market Standing
- **Valuation/Market Cap**: Not applicable (non-commercial research institute)
- **Key Metric**: Funded by MBZUAI; specific budget not publicly disclosed. Notable open-source model releases (JAIS, Vicuna, K2, PAN).
- **Notable Investors/Partners**: MBZUAI, with international advisory boards and peer review processes. Partnerships with industry leaders, academic institutions, and public organizations.
- **Growth Signals**: Launch of Silicon Valley lab in Sunnyvale, CA (2026); expansion into Paris and Abu Dhabi nodes; active hiring for research interns, distributed ML engineers, HPC engineers; release of flagship models (PAN, K2 update).

## Competitive Advantages
- **Openness & transparency**: Full release of training code, datasets, and model checkpoints through LLM360 – one of the most transparent approaches in AI.
- **Global research network**: Three hubs (Abu Dhabi, Silicon Valley, Paris) combining academic rigor with startup agility.
- **Access to large-scale compute**: High-performance computing infrastructure for training frontier models.
- **Multilingual & cultural focus**: JAIS addresses underrepresentation of Arabic and other languages, preserving cultural authenticity.
- **World model innovation**: PAN differentiates from text-only models by predicting comprehensive world states for advanced reasoning and simulation.

## Strategic Focus
- Continue building open, powerful foundation models across language, vision, multimodal, and domain-specific systems.
- Expand global collaboration and talent acquisition, especially in Silicon Valley.
- Advance responsible AI with safety systems and international advisory boards.
- Drive real-world impact through partnerships in scientific discovery, human-AI interaction, and public good.

## Why Work Here
- **Culture**: Collaborative, cutting-edge academic research environment with a mission to open-source AI for global benefit. Teams span Abu Dhabi, Paris, and Silicon Valley.
- **Remote/hybrid/office**: All listed roles are **on-site** (Sunnyvale, CA or Abu Dhabi). No remote policy indicated.
- **Notable perks**: Work with world-class researchers, access to large-scale compute, opportunity to publish and contribute to open-source models. The institute combines the agility of a startup with the resources of an established university.
- **Current openings**: AI Research Internship (LLM), Distributed Machine Learning Engineer, Eval360 Error Analysis Engineer, High Performance Computing Software Engineer (Supercomputing) – all on-site in Sunnyvale or Abu Dhabi.

## Sources
1. [ifm.ai](https://ifm.ai/)
2. [ifm.ai/about](https://ifm.ai/about/)
3. [jobs.lever.co/ifm-us](https://jobs.lever.co/ifm-us)
4. [LinkedIn – Institute of Foundation Models](https://www.linkedin.com/company/institute-of-foundation-models)
5. [MBZUAI News – Launch of IFM and Silicon Valley Lab](https://mbzuai.ac.ae/news/mbzuai-launches-institute-of-foundation-models-and-establishes-silicon-valley-ai-lab/)

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