Jobs at ifm

Build and sustain IFM’s Silicon Valley developer community through events, hackathons, online engagement, ecosystem partnerships, and member-led activities. Manage relationships with developers, founders, researchers, students, universities, accelerators, and open-source groups. Coordinate with Developer Relations, research, and Marketing teams, track engagement metrics, gather community feedback, and connect builders with technical support. A technical background in AI, computer science, engineering, or software development is preferred.

Drives developer adoption of IFM’s LLMs by supporting evaluation, integration, and sustained use. Creates technical documentation, examples, demonstrations, talks, and workshops; collaborates with open-source maintainers and ecosystem partners; investigates adoption barriers; reproduces issues; and translates user evidence into model and developer-experience improvements. Requires hands-on LLM application development, strong Python skills, technical communication, and experience influencing engineering or research priorities.

Provides strategic executive support to a Lab Director, including complex international calendar management, gatekeeping, travel and logistics coordination, stakeholder communications, meeting preparation, briefing materials, action-item tracking, and confidential correspondence. Serves as the operational bridge between global research hubs in the US, Paris, and UAE, proactively resolving scheduling and workflow issues while supporting high-stakes scientific and leadership priorities.

Manage high-volume, complex interview scheduling and deliver a polished candidate experience across global time zones. Lead full-cycle recruiting for PhD researchers and AI talent through sourcing, market mapping, stakeholder calibration, and closing. Own ATS data integrity, reporting, compliance, audit readiness, referral pipelines, and scalable recruiting processes. Represent the lab at conferences and serve as a talent brand ambassador.

Develop and optimize CUDA kernels and machine learning software for training and inference on advanced GPUs. Profile workloads, improve GPU utilization, build benchmarking and automation tools, troubleshoot system bottlenecks, support new model architectures, validate numerical and gradient correctness, review code, and contribute to technical documentation and industry events.

Develop and scale distributed machine learning infrastructure for large-scale LLM pre-training. Responsibilities include extending frameworks such as DeepSpeed and FSDP, implementing distributed optimizers, managing multi-node GPU launches, building experiment tracking and monitoring, improving reliability and performance, and maintaining data-loading and checkpointing workflows. The role requires strong Python, systems engineering, distributed training, multi-node debugging, and large-scale pre-training experience.

Build and scale end-to-end reinforcement learning infrastructure for foundation models. Responsibilities include extending distributed training frameworks, integrating rollout and inference systems, implementing reward and trajectory processing, configuring multi-node GPU launches, monitoring experiments, optimizing performance, and developing checkpointing and failure-recovery systems. The role requires strong Python and ML systems engineering experience, hands-on LLM RL pipeline ownership, and expertise with distributed training and policy optimization methods.

Owns and builds end-to-end data operations and evaluation programs for foundation models. Responsibilities include data vendor management, multi-million-dollar budget oversight, procurement and contract coordination, evaluation process design, benchmark analysis, and cross-functional collaboration with researchers and engineers. The role independently manages program scope, timelines, quality, performance, and evolving technical requirements.

Lead launch and sustaining product marketing for large language and world models. Develop positioning, messaging, go-to-market plans, technical content, campaigns, and adoption programs. Translate AI research, benchmarks, architectures, and capabilities into accessible stories for researchers, developers, enterprises, partners, and media. Coordinate cross-functional launches, support communications and executive visibility, track competitive developments, and measure awareness, engagement, and adoption.

Supports high-priority strategic initiatives involving AI commercialization, go-to-market planning, partnerships, market analysis, financial modeling, and operational programs. The role coordinates cross-functional teams, develops executive-ready materials, improves governance and processes, and translates technical AI capabilities into business value and actionable plans.

Own and maintain a high-performance website while creating visual assets, diagrams, infographics, presentation materials, and short-form videos. Monitor website performance, security, accessibility, SEO, and analytics. Collaborate with marketing, editorial, social media, and external design partners to maintain a consistent visual identity and translate complex AI research into clear, engaging content across platforms.

Build and improve high-quality LLM training datasets across pre-training, mid-training, and post-training. Responsibilities include data extraction, cleaning, filtering, deduplication, transformation, quality control, synthetic data generation, data evaluation, model experiments, and research into datasets and processing methods. Collaborate with researchers and engineers on foundation model projects while supporting inference, evaluation, and training experiments.

Design, develop, and operate state-of-the-art systems for curating, ingesting, filtering, annotating, and training on billion-scale visual data. Research areas include motion scoring, camera control, and video understanding. The role converts human data-curation efforts into durable, automated systems with quantitatively verifiable performance, contributing to foundation model and world model development.

Lead editorial vision and content strategy for a technical AI research lab. Develop the editorial voice, calendar, content engine, long-form and short-form content, multimedia storytelling, and owned channels. Translate complex research into credible, accessible communications for technical and general audiences. Partner with engineers, researchers, communications, and product teams on launches and flagship reports; manage a social media manager, build a freelance writing team, and track content performance metrics.

Manage and grow social media channels for an AI research lab, engaging developer and research communities across X, Hugging Face, Reddit, LinkedIn, Instagram, and YouTube. Responsibilities include channel strategy, launch amplification, community building, content creation, brand voice management, trend monitoring, content calendars, cross-functional execution, and analytics reporting.

Provide overnight operational coverage for large-scale GPU clusters: monitor health and performance, triage incidents, support researchers, run recovery procedures, validate deployments and upgrades, track utilization, and build automation and monitoring tools.

Build and operate Eval360 evaluation service, perform deep error analysis on model outputs, design error taxonomies and review interfaces, implement front-end and back-end evaluation pipelines, dashboards, APIs, and data workflows, and collaborate with researchers and engineering teams to improve model quality and evaluation infrastructure.

Short-term intensive research internship assisting in dataset collection and preprocessing, model architecture design and tuning, implementation of testing frameworks, analysis of model accuracy and bias, algorithm refinement, and documentation to support foundation-model research.

Lead research to improve LLM efficiency and architectures, design and implement NLP algorithms (using PyTorch), integrate LLMs with vision models, optimize models and data pipelines, collaborate on code/design reviews, publish technical reports and papers, represent the institute at conferences, and contribute to ML infrastructure and model evaluation.

Design, build, and maintain scalable ML infrastructure for training and inference of LLM, TTS, and multimodal systems. Own model lifecycle, Kubernetes-based deployments (EKS/Helm), CI/CD, monitoring, cost optimization, infra-as-code (Terraform), and collaborate with researchers to productionize models and observability pipelines.