Senior Applied AI Manager

Achievements

At Oumi, we aim to democratize access to advanced AI technologies. Our platform automates the process of creating custom AI models, making it possible to develop and deploy AI solutions quickly and efficiently.

Responsibilities

• Define and drive the research and engineering roadmap for AI science at Oumi.
• Translate company objectives into concrete milestones for model quality, capability, and efficiency.
• Recruit, manage, and develop a high-performing team of ML engineers and applied researchers.
• Lead experimentation across the full training stack, including pre-training, supervised fine-tuning, alignment techniques, distillation, curriculum learning, and data mixing.
• Own the data side of model development, building intelligent pipelines for quality scoring, filtering, deduplication, and synthetic data generation.
• Design evaluation frameworks that go beyond static benchmarks and build automated feedback loops to continuously improve model performance.
• Research and develop agent-based systems that orchestrate the model training lifecycle, enabling training runs to become smarter over time with less manual intervention.
• Partner with infrastructure and product teams to ensure AI science features ship reliably and perform at high quality.
• Publish findings, contribute to open-source tooling, and collaborate with external researchers and academic partners.

Requirements

• 5+ years of professional experience in ML research, ML engineering, or a closely related field.
• Demonstrated track record of turning research into production systems.
• PhD in AI or equivalent industry experience.
• 1+ years of experience managing engineers or applied researchers.
• Expertise across the model training lifecycle, including pre-training, fine-tuning (SFT, RLHF, DPO), evaluation, and deployment.
• Hands-on experience training or substantially improving large language models (LLMs) or vision-language models (VLMs).
• Experience designing rigorous experiments, interpreting results critically, and staying current with the literature.
• Experience building or working with LLM-powered automation, tool-use patterns, or multi-agent architectures.

Qualifications

• Ph.D. in Computer Science, Machine Learning, or a related field.
• Publications in ML/AI venues (NeurIPS, ICML, ICLR, ACL, etc.).
• Experience with data-centric ML approaches, such as data quality estimation, curriculum learning, or synthetic data generation.
• Contributions to open-source ML frameworks or tooling.
• Familiarity with ML infrastructure, including Kubernetes, GPU clusters, and orchestration frameworks.
• Experience at an early-stage or high-growth startup where you wore multiple hats across research, engineering, and strategy.