Applied Scientist

Recrew AI; Gurugram District; Presencial; INR 2 500 000 - 6 000 000; Tempo integral

Resumo da oferta

Recrew AI in Gurugram, India, seeks an Applied Scientist - Machine Learning to advance the company’s Causal Intelligence platform. You will design and ship ML systems that turn multimodal industrial data into root-cause insights used by semiconductor fabs and manufacturing lines.

You will work across time-series diagnostics, LLM/RAG pipelines, and scalable ingestion, influencing product architecture and scientific direction with direct customer impact.

Qualificações

• BS/MS/PhD in AI/ML, CS, EE, or related field.
• 2–5 years of hands-on ML research or applied ML experience.
• Strong Python with production ML systems.
• Experience with LLMs, RAG, prompts or fine-tuning.

Responsabilidades

• Design ML algorithms for time-series, logs, and waveforms.
• Build and optimize LLM/RAG pipelines with extraction & search.
• Architect multimodal ingestion for documents, logs, sensors, and images.
• Productize ML models into customer-facing systems.
• Define use cases, benchmarks, and evaluation frameworks.
• Lead ML projects end-to-end; contribute to architecture docs.
• Prototype quickly and iterate on customer feedback.

Conhecimentos

Python

Time-series modeling

LLMs

RAG

Prompt engineering

ML production

Research methods

Formação académica

BS/MS/PhD in AI/ML

Descrição da oferta de emprego

Role: Applied Scientist - Machine Learning

Location: Gurugram, India (Onsite)

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-tuningDemonstrated 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