PM.

Pranav Meraga / Engineering portfolio

Ideas into code.
Code into motion

I’m Pranav, an engineer working across machine learning, software systems, and robotics. From candidate search to robotic control, I build systems that connect research with practice.

Explore my work
UC Berkeley · Aerospace EngineeringPython / TypeScript / C / ROS 2

01 / SELECTED WORK

Built to solve.

Retrieval, reasoning, and real-world systems.

RETRIEVAL / MERCORMercor
RetrieveRe-rankMatch
02 — MACHINE LEARNINGProject made for Mercor

Better search. Better matches.

A candidate search engine combining vector and lexical retrieval, reciprocal rank fusion, cross-encoder re-ranking, and an LLM judge to evaluate role-specific criteria.

  • Python
  • Turbopuffer
  • Voyage AI
  • OpenAI
DEVELOPER TOOLS / FOUNDING MLE PROJECT
code_intelligence.py
index(apex, metadata)
  
SQLite dependency graph
  
query(symbols, relationships)
  
Compact context for agents
03 — DEVELOPER TOOLSStartup founding MLE role

Make a codebase legible to AI.

A Salesforce code-intelligence pipeline built for a founding MLE role at a startup. It indexes Apex and metadata into SQLite, then exposes deterministic, compact queries for LLM agents.

  • Python
  • SQLite
  • MCP
  • 21 tests passing
SYSTEMS / SQUARE KETTLE
Backtest.
Inspect.
Iterate.
Market data → Strategy → Evaluation
04 — SOFTWARE ENGINEERINGSquare Kettle take-home

A clearer view of a strategy.

Improvements to a supplied futures backtesting system: PnL exception handling, crossed-quote validation, multi-strategy visualization, custom indicators, and unit tests.

  • NautilusTrader
  • Python
  • React
  • Databento
REASONING / ARC-AGI
INPUT
OUTPUT
Learn the pattern. Predict the transformation.
05 — REASONINGARC interview challenge

From examples to a new answer.

An LLM agent for ARC grid puzzles, with structured prompts, text and JSON grid parsing, and an evaluation harness over 20 included puzzles.

  • TypeScript
  • Anthropic
  • OpenAI
ANOMALY DETECTION / MACROBASE
Transaction featuresIsolation ForestAlerts + explanations
Score / Classify / Explain
06 — APPLIED MACHINE LEARNINGAML transaction analysis

Find the unusual. Explain why.

A MacroBase-inspired pipeline that scores transactions, compares percentile thresholds, and explains anomalies with overrepresented categorical attributes.

  • Isolation Forest
  • pandas
  • scikit-learn

02 / RESEARCH

Where it started.

Machine learning meets engineering data.