Senior Applied AI Manager

Achievements

At Oumi, we aim to democratize access to advanced AI technologies. Our platform enables users to build custom AI models quickly and efficiently, automating key stages from data synthesis to model deployment.

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 to orchestrate the model training lifecycle, making training smarter over time with minimal 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 to represent Oumi's AI science work in the broader research community.

Requirements

Experience: 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.

Management: 1+ years of experience managing engineers or applied researchers. You've hired, coached, and retained strong technical talent.

ML Depth: Expertise across the model training lifecycle—pre-training, fine-tuning (SFT, RLHF, DPO), evaluation, and deployment. Hands-on experience training or substantially improving LLMs or VLMs.

Research Mindset: You design rigorous experiments, interpret results critically, and stay current with the literature. You know when to apply an existing technique and when to invent something new.

Agentic Systems: Experience building or working with LLM-powered automation, tool-use patterns, or multi-agent architectures. You think naturally about how to decompose complex tasks into agent-friendly steps.

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—data quality estimation, curriculum learning, or synthetic data generation.

Contributions to open-source ML frameworks or tooling.

Familiarity with ML infrastructure (Kubernetes, GPU clusters, orchestration frameworks).

Prior experience at an early-stage or high-growth startup where you wore multiple hats across research, engineering, and strategy.