This role is for one of the Weekday's clients. As a Founding Applied Scientist, you will operate at the intersection of advanced research and production engineering, building core systems that enable AI teammates to learn from enterprise environments, reason over tribal knowledge, and deliver measurable business impact.
Requirements
- Research, design, and ship next-generation system architectures focused on Agentic & Tribal Knowledge Systems
- Design and implement multi-agent architectures capable of solving complex, long-horizon tasks
- Develop systems that integrate organizational memory and domain knowledge into intelligent workflows
- Identify, scope, and solve complex business problems using machine learning
- Drive improvements in engagement, retention, pricing, optimization, and other core metrics
- Deliver measurable top-line impact at scale
- Partner directly with engineering and product teams at strategic customers
- Serve as a trusted advisor on ML architecture and agent-based systems
- Guide adoption of production-ready agentic AI solutions
- Design, build, and deploy production-grade ML systems
- Extend platform capabilities to support large-scale, user-centric environments
- Own the full lifecycle from experimentation to scalable deployment
- Experience operating at large scale (100M+ users or equivalent system complexity)
- Production Engineering
- Engineer-first mindset with strong coding ability in Python and/or C++
- Experience building low-latency inference systems
- Familiarity with distributed computing frameworks such as Ray, Spark, or Flink
- Proven ability to write production-grade, maintainable systems
- Full ML Lifecycle Expertise
- Experience with feature stores, real-time data pipelines (Kafka, Beam), and experimentation frameworks
- Deep understanding of online vs. offline evaluation methodologies
- Experience designing A/B testing systems and monitoring feedback loops in production
- Strong grasp of model observability and reliability in live environments
- Algorithmic Depth
- Strong foundations in large-scale ML systems (embeddings, retrieval and ranking, GNNs, bandits)
- Experience with modern AI stack components including LLMs, reinforcement learning, and multi-agent orchestration
- Technical Strategy
- Experience defining architectural standards and technical roadmaps
- Ability to balance trade-offs between model complexity, latency, reliability, and development velocity
Benefits
- Founding-level equity and meaningful ownership
- Opportunity to solve hard, unsolved problems in agentic reasoning, memory systems, and reinforcement learning
- Collaboration with a dense, high-caliber team of researchers and engineers who have built and scaled systems serving hundreds of millions of users
- Inclusive and equal opportunity workplace committed to diversity
