Hello, I'm Sahil Menon
Computer Engineering student at UNSW, focused on embedded hardware, AI, and autonomous systems.
About
Who I am
I’m Sahil, a first-year Computer Engineering student at UNSW, building towards a future in robotics and intelligent systems.
My main interest is autonomous machines: where embedded hardware, low-level software, and AI meet. I’m looking for research and internship opportunities in robotics and AI.
Outside of engineering, I led BuildingBloCS, Singapore’s largest student-computing conference, which taught me what it takes to make technical work reach people at scale.
Sydney, Australia · Australian permanent resident · Singapore citizen
Education
BE (Computer) (Honours)
University of New South Wales, Sydney
2026 – 2029
Raffles Institution
Singapore, 96th percentile
2024 – 2025
Open to
Software · systems · AI internships
Sydney or remote
Summer 2026–27 (Nov–Feb)
Technical Skills
- C / C++ / Python
- TypeScript / JS / Java
- React / Astro / FastAPI
- TensorFlow / PyTorch
- NumPy / Pandas / SciPy
- AWS / Kubernetes / Terraform
- Docker / Redis / CI-CD
- SQL / Git / Linux
GitHub activity · last 6 months
Highlights
- Top 10% worldwide of 22,000+ teams, IMC Prosperity 4
- UNSW CSESoc Flagship Hackathon finalist
Experience
BuildingBloCS
Overall-In-Charge
Led Singapore’s largest student-led Computing Advocacy Program, personally overseeing annual conferences of 1,000+ participants from 60+ schools. Coordinated organisers from 30+ institutions and secured $200,000+ in sponsorships to fund industry expert talks, hackathon judging, and national-scale mentorship programs. Designed and delivered technical workshops, including AI Safety & LLM Prompting for 200+ participants and an advanced PyTorch deep-learning session.
Walled AI
AI Safety Researcher
Investigated LLM hallucination failure modes in context-based QA over a 6-month engagement under guidance from a research lead. Designed and built a 50,000+ entry evaluation benchmark, evaluating Lynx and HaluBench across precision, recall, and contextual faithfulness metrics to expose systematic accuracy gaps.
Featured projects
PromptCoach
CLI + Chrome extension that coaches developers toward efficient AI prompting — UNSW CSESoc Flagship Hackathon finalist (team project).
How it works
AI-assisted coding wastes tokens on bloated prompts with no feedback loop. PromptCoach turns that repeated waste into a one-time cost.
One shared analysis core behind a CLI and Chrome extension (TypeScript/Next.js + FastAPI, Drizzle ORM on Cloudflare D1). Coaching is opt-in via a review: prompt prefix — ordinary prompts never leave the machine; a local bridge falls back to offline heuristics. Hosted analysis runs through Anthropic’s Batches API on Haiku at ~2% of a frontier model’s energy per task, with environmental impact reported as a sourced, uncertainty-labelled range. Local-first, no telemetry.
IMC Prosperity 4: Algorithmic Trading
Solo competitor in IMC Prosperity 4, finishing top 10% worldwide and top 200 in Australia out of 22,000+ global teams across 5 rounds of algorithmic and manual trading.
How it works
The challenge: keeping a market-making engine profitable across changing volatility regimes without overfitting to any single one.
Built a three-tier market-making engine (take/clear/make) using Welford online mean, online AR(1) on price deviations, z-score tiered sizing, and asymmetric bid/ask anchoring. Built a separate trend-following MM with hardcoded-slope discovery, online OLS blending (70/30), and full-book order imbalance microprice adjustment.
Streaming Maze Engine
Full-stack maze platform with a C++20 generator hitting ~38 Mcells/s single-threaded (≈2× a published C# baseline) and ~92 Mcells/s on 8 cores, streaming 10-billion-cell mazes in O(width) memory, with real-time multiplayer and a WebGL2 renderer.
How it works
Generating a 10-billion-cell maze at full resolution should take terabytes of memory. Getting it under tens of MB while supporting thousands of real-time players is the actual constraint.
Eller’s algorithm with within-row strip parallelism and AVX2/BMI2 SIMD packing scales to ~92 Mcells/s on 8 cores in O(width) memory. FastAPI + Redis pub/sub fan-out across K8s pods (p99 3.3 ms at 100 concurrent bots); WebGL2 renderer batches an entire 64×64 chunk into one GPU draw call at 60 fps. One-command AWS deploy via Terraform + EKS.
Slide Games
Python framework (published on PyPI) that compiles arcade game logic into fully playable Google Slides via BFS state enumeration: one slide per reachable state, hyperlink-navigated.
How it works
No runtime, no JavaScript, no server. Just a shareable URL that plays a full arcade game.
1,000-state ceiling bounds exponential growth (Pac-Man scales as positions × 2ⁿ with n pellets). Token-bucket rate limiter at ≤50 API writes/min with 5 concurrent batch uploads generates ~500-state presentations in 1–3 min. Pygame-inspired 1920×1080 rendering API with 40+ colours and 3 themes; campaign system across 4 bundled games (491–600 states each).
PixelVault
Python file-to-video codec that encodes any file into MP4 for lossless storage on YouTube, recovering the original file exactly despite H.264/VP9 re-encoding.
How it works
YouTube lossy-compresses every uploaded video. Storing arbitrary binary data there without any corruption is the problem.
2×2 uniform pixel blocks survive ±127 DCT luma ringing in H.264/VP9. Three-tier Reed-Solomon ECC over GF(2⁸) (vectorised → Berlekamp-Massey → parallel) with byte interleaving for burst-error recovery. AES-256-GCM + PBKDF2-SHA256 encryption, zlib compression, and 38× faster encoding at 0.82–2.88 MB/s via NVENC/AMF/QSV hardware acceleration with YouTube OAuth2 upload.
ASCII / Unicode Art Converter
Zero-dependency, fully client-side ASCII/Unicode art converter: 7 character modes, 3,163-codepoint Unicode pool, 123-emoji mosaic, image/video/webcam input.
How it works
The problem: faithfully mapping full-colour images to text characters at 30 fps without losing perceptual detail.
O(1) nearest-colour lookup via a precomputed 32³ = 32,768-entry RGB quantisation table enables 30 fps video at ~2.1 ms/frame. Implements Floyd-Steinberg, Atkinson, and Bayer dithering, Sobel edge detection, and a particle drift system. Supports 6 export formats (PNG, SVG, TXT, WebM via MediaRecorder API) and reports live render time at 1920×1080.
Have an internship, research role, or project in mind?
Contact me