Senior Research Engineer

Rempact; Bengaluru South; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Rempact in Bangalore is seeking a Senior Research Engineer to build applied ML and NLP systems for oncology-focused clinical information extraction and real-world data workflows. This is a hands-on role that moves between research, prototyping, evaluation, and production collaboration.

You will extract structured information from clinical records such as notes, pathology reports, molecular reports, imaging data, and scanned documents, partnering with Research Engineers, Clinical AI Data

Qualifications

• 4-7+ years of hands-on ML/NLP/LLM engineering, preferably with production systems.
• Strong Python and PyTorch experience; write clean, reusable code beyond notebooks.
• Solid understanding of LLMs, transformers, embeddings, RAG, fine-tuning, and evaluation.

Responsibilities

• Design and implement LLM/NLP-based information extraction systems for oncology and RWE use cases.
• Build and improve pipelines for extraction, normalization, summarization, patient-trial matching, and structured abstraction.
• Collaborate with ML Evaluation Engineers to validate model quality and release readiness.
• Collaborate with Data Engineers to move models into production.
• Write design docs, review code, and mentor junior engineers.

Skills

Python

PyTorch

LLM / NLP

Engineering practices

Mentoring

Tools

LangChain

vLLM

Ray

Triton

Vector databases

MLflow/W&B

LlamaIndex

Job description

Position: Senior Research Engineer

Location: Bangalore

Working Days: Monday to Friday

Experience Required: 4-7 years

GROWTH PATH

This is an individual contributor role with strong ownership expectations. High performers may be considered for workstream lead or functional lead responsibilities after approximately 12 months (6 months for exceptional candidates), based on demonstrated ownership, delivery, technical judgment, mentoring, cross-functional influence, and ability to reduce dependency on the Director of ML.

About The Role

We are looking for a Senior Research Engineer to build applied ML and LLM systems for oncology-focused clinical information extraction, real-world evidence workflows, patient-trial matching, registry/QI abstraction, and clinical data products. This is a hands-on role for someone who can move between research, prototyping, evaluation, and production collaboration.

You will work on extracting structured, evidence-backed information from complex longitudinal clinical records such as notes, pathology reports, molecular reports, imaging reports, scanned documents, and other oncology data sources. You will partner closely with Research Engineers, Clinical AI Data Specialists, ML Evaluation Engineers, MLOps/Data Engineers, and clinical experts.

What You Will Do

• Design and implement LLM/NLP-based information extraction systems for oncology and RWE use cases.
• Build and improve pipelines for entity extraction, normalization, summarization, patient-trial matching, eligibility reasoning, and structured clinical abstraction.
• Explore prompting, fine-tuning, RAG, agentic workflows, constrained structured outputs, weak supervision, active learning, and hybrid ML/rule-based systems.
• Define experiments, baselines, ablations, error-analysis plans, and practical evaluation approaches.
• Collaborate with Clinical AI Data Specialists to convert clinical requirements into ML-feasible extraction tasks and labeling guidelines.
• Collaborate with ML Evaluation Engineers to validate model quality, regression behavior, segment-level performance, and release readiness.
• Collaborate with MLOps/Data Engineering to move reliable models and workflows into production.
• Write technical design documents, review code, mentor junior engineers/interns, and drive bounded workstreams independently.
• Over time, demonstrate readiness to own larger technical workstreams end to end.

What We Expect

• 4-7+ years of hands-on experience in ML/NLP/LLM engineering, preferably with applied production or near-production systems.
• Strong Python and PyTorch experience; ability to write clean, reusable, testable code beyond notebooks.
• Strong understanding of LLMs, transformers, embeddings, RAG, fine-tuning, prompt engineering, evaluation, and structured output generation.
• Ability to reason about model errors, data quality, annotation ambiguity, hidden test sets, and clinical edge cases.
• Experience building reusable ML pipelines and experimental frameworks.
• Strong engineering habits: versioning, tests, documentation, reproducibility, and peer review.
• Ability to mentor junior engineers and communicate technical tradeoffs clearly.

NICE TO HAVE

• Clinical NLP, biomedical NLP, healthcare data, oncology data, RWE, EHR data, pathology, biomarkers, cancer registry abstraction, or clinical trial eligibility experience.
• Experience with LoRA/SFT/DPO, synthetic data generation, weak supervision, active learning, human-in-the-loop systems, or preference optimization.
• Experience with vLLM, Ray, Triton, vector databases, LangChain/LlamaIndex, MLflow/W&B, or similar tooling.
• Experience writing technical design docs and leading technical discussions.

SUCCESS IN 6 MONTHS

• Owns at least one major ML workstream with limited day-to-day supervision.
• Produces reliable design docs, experiment plans, and release-ready model improvements.
• Mentors junior REs or interns effectively.
• Reduces dependency on the Director of ML for implementation details.
• Demonstrates readiness for workstream lead or technical lead responsibilities.