Role: Applied Scientist – Machine Learning
Function: Machine Learning / Applied Science
Location: Gurugram , India
Type: Full-time
Industry: Information Technology & Services, Computer Software, Industrial Automation
About Company
An industrial AI startup using Causal AI to help semiconductor and manufacturing companies find and fix the root causes of equipment and process failures. The company's Causal Intelligence Platform integrates multimodal factory data with explainable causal reasoning — going beyond surface-level correlations.
It is seed-funded at $3M and backed by Capria Ventures and Endiya Partners. The team of 19 engineers brings deep expertise in semiconductor defect analysis, causal reasoning, and graph intelligence.
The company operates with high agency, low bureaucracy, and a real-world-first culture. Solutions are built for explainability and deployed in secure, private environments — co-developed closely with customers.
Position Overview
This role sits at the intersection of applied ML research and production engineering, directly shaping the scientific direction of the company's Causal Intelligence Platform. The Applied Scientist will design and ship ML systems that turn chaotic multimodal industrial data — time-series, logs, waveforms, and documents — into reliable root-cause insights used by engineers at semiconductor fabs, utilities, and advanced manufacturing lines. The work spans LLM/RAG pipelines, time-series diagnostics, and scalable multimodal ingestion, with direct influence on product architecture and customer outcomes.
Role & Responsibilities
- Design and develop ML algorithms for time-series, log, and waveform-based diagnostics across industrial datasets
- Build and optimize LLM/RAG pipelines including structured extraction, hybrid search, and domain-specific reasoning
- Architect scalable multimodal ingestion frameworks covering documents, logs, sensor streams, alarms, and images
- Productize ML models in collaboration with engineering teams into reliable, customer-facing systems
- Define use cases, benchmarks, and evaluation frameworks with product teams
- Lead ML projects end-to-end and contribute to patents, technical architecture documents, and research publications
- Prototype aggressively, iterate on real customer feedback, and own outcomes from research through deployment
Must Have Criteria
- BS/MS/PhD in AI/ML, Computer Science, Electrical Engineering, Applied Mathematics, or a related field
- 2–5 years of hands-on experience in ML research or applied ML roles
- Strong Python programming skills with a track record of building and scaling ML systems in production
- Hands-on experience with LLMs, RAG architecture, prompt engineering, or fine-tuning
- Demonstrated experience with time-series modeling, anomaly detection, or statistical signal processing
- Proven ability to take ML projects from experimentation to deployed, customer-facing systems
Nice to Have
- Startup or early-stage product-building experience
- Background in semiconductor, manufacturing, battery systems, or industrial data domains
- Experience building agents or autonomous reasoning systems
- Contributions to open-source ML or LLM tooling
- Direct experience working with customers or field engineering teams
What We Offer
- Frontier ML problems on multimodal industrial data with real, measurable customer impact
- High agency and direct influence on product and scientific direction at a seed-stage company
- ESOPs and rapid growth opportunities