Oumi
Senior Applied AI Manager
San Mateo, CA · Senior
Sponsorship not specifiedDetected 113 days ago
KubernetesMachine LearningLLMsAgentic AIAI OrchestrationA/B TestingResearchLeadershipCollaboration
About the role
- Your scope spans the full model development lifecycle-data strategy, pre-training and post-training methodology, evaluation science, and production deployment-as well as the agentic systems that automate and improve each stage.
- You'll work closely with the CEO and product leadership to translate Oumi's company strategy into a concrete AI science roadmap, then execute against it with a growing team of ML engineers and applied researchers.
- This role blends research and product shipping.
Responsibilities
- AI Science Strategy & Roadmap: Define and drive the research and engineering roadmap for AI science at Oumi.
- Team Building: Recruit, manage, and develop a high-performing team of ML engineers and applied researchers. Set a high bar for talent, create an environment of rigorous experimentation, and coach people toward increasing scope and independence.
- Data Strategy: Own the data side of model development.
- Build intelligent pipelines for quality scoring, filtering, deduplication, and synthetic data generation.
- Develop a data-scientific understanding of what data actually moves the needle and use it to guide investment.
- Evaluation & Feedback Loops: Design evaluation frameworks that go beyond static benchmarks. Build automated feedback loops where evaluation signals inform data selection, training decisions, and agent behavior-creating a flywheel of continuous improvement.
- Agentic Workflows: Research and develop agent-based systems that orchestrate the model training lifecycle-from automated hyperparameter optimization to self-improving data curation-so training runs get smarter over time with less manual intervention.
- Production & Deployment: Partner with infrastructure and product teams to ensure AI science features ship reliably, perform at quality.
- Open Source & Community: Publish findings, contribute to open-source tooling, and collaborate with external researchers and academic partners. Represent Oumi's AI science work in the broader research community.
- 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.
Requirements
- 1+ years of experience managing engineers or applied researchers.
- Management: 1+ years of experience managing engineers or applied researchers.
- You know when to apply an existing technique and when to invent something new.
Nice to have
- Publications in ML/AI venues (NeurIPS, ICML, ICLR, ACL, etc.).
- 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.
Skills
- Design evaluation frameworks that go beyond static benchmarks.
Company info
- you'll set the applied science agenda, build and lead the team, and be accountable for the science quality of every feature that ships on our platform.
- You'll stay very close to the academic research, but also industry trends.
- You will leverage AI science, drive experimentation, and translate breakthroughs into production systems that Oumi and our customers use every day.
- AI Science Strategy & Roadmap: 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-and make the hard prioritization calls when resources are scarce.
- Training Science: Lead experimentation across the full training stack-pre-training, supervised fine-tuning, alignment (RLHF, DPO, GRPO), distillation, curriculum learning, and data mixing-to systematically improve model quality with each generation.
- Data Strategy: Own the data side of model development. Build intelligent pipelines for quality scoring, filtering, deduplication, and synthetic data generation. Develop a data-scientific understanding of what data actually moves the needle and use it to guide investment.
This listing is sourced directly from Oumi's careers page and normalized into a canonical job model.