Senior Research Engineer

PinnacleU
HSR Layout, Bengaluru, Karnataka

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Job details

Full-time
₹32,00,000 - ₹55,00,000 a year
4 days ago

Qualifications

Law
PyTorch
Master's degree
Natural language processing
Clinical trials
AI
Python

Full 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.

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GROWTH PATH

ABOUT THE ROLE

WHAT YOU WILL DO

WHAT WE EXPECT

NICE TO HAVE

SUCCESS IN 6 MONTHS

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