Founding Applied Scientist

This role is for one of the Weekday's clients

Salary range: Rs 5000000 - Rs 10000000 (ie INR 50 - 100 LPA)

Min Experience: 4 years

Location: Bengaluru

JobType: full-time

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.

This is a high-autonomy, builder-first role designed for someone who wants to move beyond static benchmarks and tackle the “last mile” challenges of AI reliability, memory, and agency in real-world enterprise systems. You will help define how applied science is practiced in the emerging era of intelligent agents.

Requirements:
What You’ll Build

You will 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

High-Impact Applied Science Solutions

• 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

Customer-Facing Applied AI

• 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

End-to-End Model Development

• 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

Core Qualifications

Experience & Impact

• 4+ years in Applied Science or ML Engineering
• Demonstrated track record of shipping ML systems that drove measurable business outcomes (e.g., retention, engagement, revenue)
• 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

Nice to Have

• PhD or M.S. in Computer Science, Statistics, or related quantitative discipline
• Experience at a frontier AI lab or high-growth AI startup
• Publications in leading ML conferences (NeurIPS, ICML, ICLR, KDD, RecSys)
• Background in recommender systems, personalization, causal inference, or computational advertising

Why Join

• 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

Core Skills

• Applied Science
• ML Engineering
• Agentic Systems
• Python
• C++
• Retention & Engagement Modeling
• Distributed Systems