Available now · Bristol, UK & Remote

Rahul Surya

I work out whether ML systems actually do what the numbers say: LLM evaluation and monitoring, inference performance, and parallel code that holds up on a supercomputer.

CURRENTLY BUILDING: LLM serving load tests: batching vs KV-cache fit View Board →
COSMIC ANOMALY TELEMETRY HUD CLICK TO CYCLE STATE: ACTIVE
ACTIVE ANOMALY SCHWARZSCHILD BLACK HOLE
SINGULARITY MASS 1.50 M☉
SPIN VELOCITY 0.00 J

The Engineer Behind
the Algorithm

Most of my work lives somewhere between ML and systems: training pipelines that actually run fast, parallel code that doesn't embarrass itself on a supercomputer, APIs that hold up under load.

I've just finished an MSc in High Performance Computing with Data Science at EPCC, University of Edinburgh, and I'm now based in Bristol. Before that I built a lightning-prediction research prototype at ISRO's National Remote Sensing Centre and shipped client software in two internships.

I care about the stuff underneath the model: memory, scheduling, latency. If it runs on a cluster, I've probably optimised it.

6 Industry Certifications
703 K Telemetry records audited (MSc)
16 Ranks Parallel Scale @ ARCHER2

Tech Stack

Languages

Python C/C++ Java SQL Bash

ML / AI

PyTorch TensorFlow Hugging Face Scikit-learn

HPC

MPI OpenMP CUDA ARCHER2 Slurm

Infrastructure

Azure Docker Kubernetes CI/CD

Data

Spark PostgreSQL MongoDB Redis Kafka
// Foundational / coursework stack:
Kafka MongoDB TensorFlow Spark Hadoop

What I've
Built & Shipped

Measurement studies, research, and deliberate engineering experiments.

Shipped & Research

Explorations

Research Spikes

Deliberate prototypes and targeted engineering experiments.

Loading explorations…

Distributed Systems
Simulation

An interactive Raft Consensus Cluster visualising elections, replication, partitions, and state recovery.

Simulation: Active
Node 1: Click to toggle offline
Leader
Candidate
Follower
Offline
[INFO] Cluster initialised with 5 nodes.
[INFO] Starting randomized election timeouts (3.5s - 5.5s)...
package raft

import "sync"

type State int
const (
    Follower State = iota
    Candidate
    Leader
)

type LogEntry struct {
    Term    int
    Command string
}

type Raft struct {
    mu        sync.Mutex
    id        int
    peers     []int
    state     State
    
    // Persistent consensus state
    currentTerm int
    votedFor    int
    log         []LogEntry
    commitIndex int
}

// AppendEntries RPC handles log replication & heartbeats
func (rf *Raft) AppendEntries(args *AppendEntriesArgs, reply *AppendEntriesReply) {
    rf.mu.Lock()
    defer rf.mu.Unlock()
    
    // Reject outdated leader terms
    if args.Term < rf.currentTerm {
        reply.Success = false
        return
    }
    
    // Reset election timer
    rf.heartbeatCh <- true
    
    // Truncate logs if conflict exists & append new entries
    if len(args.Entries) > 0 {
        rf.log = append(rf.log[:args.PrevLogIndex+1], args.Entries...)
    }
    
    // Commit and apply to state machine
    if args.LeaderCommit > rf.commitIndex {
        rf.commitIndex = min(args.LeaderCommit, len(rf.log)-1)
    }
    reply.Success = true
}

Where I've
Worked

MSc Student: High Performance Computing with Data Science

University of Edinburgh · EPCC

2025 – 2026

Parallel programming on ARCHER2 (the UK national supercomputer), MPI and OpenMP, CUDA, and data science. Dissertation on catching container memory leaks early, evaluated at a fixed false-alarm budget. Submitted August 2026.

MPIOpenMPCUDAARCHER2Data Science

ML Engineer Intern

ISRO: National Remote Sensing Centre

Oct 2023 – Dec 2023

Trained a ConvLSTM encoder-decoder in PyTorch on WRF weather-model output fused with ground-sensor records of 163,000+ lightning strikes, comparing three architectures, with a React and Leaflet dashboard. Submitted as a research prototype to ISRO's internal research library.

PyTorchConvLSTMReactResearch Prototype

Data Science Intern

Clustor Computing

Jun 2023 – Sep 2023

One of a team of five on financial time-series reporting. I built ETL stages and the Power BI and Tableau dashboards that five non-technical colleagues used daily, and rebuilt them twice after asking what they actually read. The team's forecasting pipeline reached 84% directional accuracy.

ETLPower BITableau

Software Engineer Intern

Develoscope Software Solutions · remote, concurrent

Jun 2023 – Sep 2023

Shipped four Java web applications (JSP, Tomcat) to paying clients, taking requirements from them directly and staying on as their support contact after handover.

JavaJSPTomcatFull-Stack

Credentials &
Certifications

Let's Build
Something

Looking for someone who can tell you whether a model is really working, and prove it? I'm based in Bristol and open to full-time roles in the UK and remote.

✦ ✦ ✦ ? z z z ?
\n