Atul Agarwal

Control Systems & Software Engineer

Control systems by training. Eight years shipping since — three in silicon, five in production software. Most useful where those layers meet: code that has to be correct and on time.

Get in Touch →

About

I am a generalist engineer — control theory, chip design and production software, in that order, each learned from zero.

I studied electrical engineering at IIT Jodhpur, then a master's in control & automation at IIT Delhi. Two papers came out of it: sliding-mode control of a converter feeding a destabilising load, validated in hardware-in-the-loop, and system identification — recovering a system's dynamics from input–output data without assuming the model.

None of that prepared me for MediaTek. Digital design is its own discipline and I learned it on the job — three years of timing closure, RTL verification and physical design, shipping across multiple production SoC tapeouts.

In 2021 I taught myself software engineering from scratch, again with nothing carrying over, and moved into DeFi as a smart contract engineer. I went on to write the core contracts for Hubble Exchange, a perpetuals DEX that cleared $150M+ in volume, and then co-founded Moar Market, a lending protocol on Aptos that peaked at $7.5M TVL — building it in Move along with the backends, liquidation engine and risk monitoring it needed to stay up. The code is public, adversarial and holds user funds directly — a single bug is not a ticket in a backlog, it is money gone in minutes. Four firms have audited my contracts, with no loss-of-funds findings.

Three fields, none of them handed to me by the last one, and I got good at each. What I want now is the opposite problem: robotics is the first thing I have worked on that needs all three at once. Most recently I built a real-time C++ and ROS 2 motion control stack — 200 Hz on a PREEMPT_RT kernel, computed torque with online payload estimation, taken from a MuJoCo model to a real 5-DOF arm. The estimator that works in simulation recovers almost nothing on hardware; the writeup covers the four reasons why and what replaced it.

Control theory, hardware and production software are usually three different people. I am looking for work where they have to be one. Based in India, open to international relocation.

Robotics & Control

Payload-Adaptive Motion Control — SO-ARM101
Real-time C++ / ROS 2 control stack, sim to hardware
2026

One control loop, written once, driving two plants behind a single PlantInterface: a MuJoCo model and a real 5-DOF arm over a Feetech servo bus. Runs at 200 Hz on a SCHED_FIFO thread under a PREEMPT_RT kernel — that is the runner today; the tracking figures below are not real-time measurements. Controllers — PD, computed torque, and adaptive computed torque with online payload estimation — never learn which side of the plant boundary they are on.

  • Computed torque vs naive PD (simulation, 200 g known payload): settled arm RMS 0.0216 → 0.0028 rad (−86.9%), end-effector miss 0.0300 → 0.00018 m (−99.4%), gated by a blocking rigid-body equivalence test — worst disagreement 1.33e-15 N·m on gravity, 8.66e-13 N·m on the nonlinear bias — before any controller runs
  • Online payload RLS (simulation): recovers 102.4% of the RMS gap between the empty-model controller and the perfect-model bound; identifies a 200 g payload as 202.3 g
  • The sim-to-real finding: that same estimator returns 0.8 g for a 90 g payload and 9.4 g for a 180 g one on hardware — for four coupled reasons, chief among them that commanded torque is a position overlay rather than plant torque, and that acceleration from a 12-bit encoder carries ~13× more noise than signal inside the regressor
  • The replacement, closed loop: identify mass statically from motor current at rest, calibrate the instrument, freeze, then track — cutting arm RMS a further 31% at 90 g and 45% at 180 g against computed torque with no payload model, with the payload identified as 82.8 g and 176.0 g against true masses of 90 g and 180 g
  • Real-time characterisation: wakeup jitter p99 6.18 µs and p99.9 11.34 µs on a 5000 µs period, against a 2.14 ms serial round-trip — the scheduler is ~189× cheaper than the bus, which is what locates the real bottleneck
  • Instrumented for honesty: current logs report stale reads, truncated commands, bridge saturations and missed deadlines in its log header, so a result cannot silently rest on fabricated samples
C++17 ROS 2 Jazzy PREEMPT_RT Pinocchio MuJoCo Computed Torque RLS Estimation Serial Bus / Feetech
Read the full writeup Source & data

Earlier Experience

Eight years of shipping: three designing silicon at MediaTek, five building production financial systems. Different domains, same demands — hard constraints, externally audited correctness, and systems that fail expensively when the reasoning is wrong.

Moar Market
Co-Founder & CTO
2024 – 2026

A leveraged lending protocol on Aptos — collateral vaults, variable-rate interest models, oracle integrations (Pyth, Switchboard), and a liquidation engine. Peaked at $7.5M TVL with $60M cumulative borrowed.

  • Designed and shipped the full protocol in Move: collateral vaults, variable-rate interest models, oracle integrations (Pyth, Switchboard), and liquidation engine — peaked at $7.5M TVL with $60M cumulative borrowed
  • Built an internal AI agent with full protocol context to debug failed transactions and explain PnL drops or gains
  • Defined cryptoeconomic risk parameters, collateral factors, and liquidation incentive curves for multi-asset markets — publicly highlighted by the Aptos Foundation at the $5M TVL milestone
  • Coordinated end-to-end security audit with MoveBit (no loss-of-funds findings); used LLM agents as first-pass PR reviewers and for pre-audit security analysis
  • Built backend distributed systems in TypeScript: indexer pipeline, liquidator bot, risk monitoring, and REST API with retry logic, state management, and failure handling for production reliability
Move / Aptos TypeScript Leveraged Lending Liquidation Engine Security Audits
@MoarMarket
Deploy Trade
Side Project
2026
  • Built an MCP server that exposes DeFi execution strategies as tools callable by LLM agents, enabling agents to plan and run onchain trading workflows end-to-end
  • Underlying execution layer: event-driven grid trading bot on Hyperliquid perpetual futures (TypeScript, RabbitMQ, PostgreSQL, WebSocket-based order lifecycle management)
  • Designed for production reliability: state persistence, retry logic, and order reconciliation across bot crashes and exchange disconnects; observability and tracing instrumented across critical paths
MCP Server TypeScript Hyperliquid RabbitMQ PostgreSQL
GitHub
Hubble Exchange
Protocol Engineer
2021 – 2024

Perpetual futures DEX on Avalanche with $150M+ in cumulative volume. Built a decentralized limit order book (DLOB) on Hubblenet, a custom Avalanche Subnet, in V2 — after proving the model with a Curve-style vAMM in V1. Coordinated three external audits (Dedaub, Sherlock, Code4rena) with no critical findings.

V2: Decentralized Limit Order Book (DLOB) on Avalanche Subnet
  • Owned all Solidity smart contracts for the order-book DEX on Hubblenet — a custom Avalanche Subnet (10M+ transactions) where validators doubled as order matchers
  • Wrote custom precompiles in Go for Hubblenet's Subnet EVM (a geth/coreth-based client), pushing order book matching and margin logic directly into the execution layer for performance at scale
  • Built a LayerZero cross-chain asset bridge (lock-mint) that secured multi-million-dollar transfers with no incidents over 2+ years
  • Built liquidator bot, order-matching service, and monitoring infrastructure; coordinated Sherlock and Code4rena audits end-to-end
V1: Virtual AMM Perpetuals
  • Implemented Curve-style vAMM using CurveCrypto invariant — ported complex math (Newton's method iterative solver) to Solidity for on-chain price computation
  • Designed margin engine, funding rate mechanism, and multi-collateral vault system; coordinated Dedaub audit end-to-end
Solidity Avalanche Subnets DLOB LayerZero Bridge CurveCrypto vAMM Security Audits
@HubbleExchange
DefiDollar
Smart Contract Engineer
2021

Developed Solidity smart contracts for DUSD, a stablecoin index on Ethereum (later expanded to BSC) — aggregated yield-bearing stablecoins into a diversified, risk-adjusted token. Product suite peaked at ~$45M+ TVL, with audits by PeckShield and Quantstamp. First production Solidity role after transitioning from hardware engineering.

Solidity Ethereum / BSC Yield Aggregation
@defidollar
MediaTek
Chip Design Engineer
2017 – 2020

Designed digital circuits for production SoCs at one of the world's largest semiconductor companies. Worked on timing closure, RTL verification, and physical design across multiple chip tapeouts.

VLSI / RTL SoC Design Timing Closure

Publications

Peer-reviewed control-systems research, first author on both.

  • Nonparametric Analysis of Nonlinear Distortions for Biomolecular Systems
    Atul Agarwal, Abhishek Dey, Rishi Relan, Shaunak Sen — IFAC-PapersOnLine 51(1), 313–318 (2018). IIT Delhi · DTU Compute · doi:10.1016/j.ifacol.2018.05.035

    Frequency-domain system identification. Using broadband random-phase multisine excitation to detect and quantify nonlinear distortion in biomolecular circuits without assuming a model structure — so a modeller can decide whether a linear model is still valid at a given operating point before committing to expensive nonlinear identification. The same question, asked of a robot arm, is what the payload-estimation work below is doing.
  • Sliding Mode Control of Bidirectional DC/DC Converter with Constant Power Load
    Atul Agarwal, Koyinni Deekshitha, Suresh Singh, Deepak Fulwani — 2015 IEEE First International Conference on DC Microgrids (ICDCM), Atlanta, GA, USA, June 2015. IIT Jodhpur · doi:10.1109/ICDCM.2015.7152056

    Nonlinear robust control of a plant that is destabilising by construction: a constant-power load presents negative incremental impedance, so current rises as voltage falls and conventional linear PID control is insufficient. Designed a sliding-mode controller for the bidirectional buck-boost converter interfacing storage in an islanded DC micro-grid, proved existence of the sliding mode and stability of the chosen switching surface, and validated it in real-time hardware-in-the-loop simulation on an OPAL-RT digital simulator.

Skills

Control & Signals

  • Sliding-mode and nonlinear robust control — published, HIL-validated
  • System identification — frequency-domain, multisine excitation, best linear approximation
  • Kalman and particle filtering; recursive least squares for online estimation
  • Computed torque / inverse dynamics; minimum-jerk trajectory generation

Real-Time & Robotics

  • C++17 on PREEMPT_RT — SCHED_FIFO, mlockall, absolute-deadline loops
  • ROS 2 Jazzy; Pinocchio and MuJoCo for dynamics and simulation
  • Serial servo bus bring-up — protocol, timing budgets, encoder calibration
  • From one project, not from production experience — see the writeup

Languages

  • TypeScript, Python — backend services, bots, APIs, analysis
  • Solidity — Foundry, Hardhat, EVM internals
  • Move — Aptos framework, resource model
  • C++, Go, Rust

Digital Hardware

  • Verilog / RTL design for production SoCs — three years, multiple tapeouts
  • Static timing analysis and timing closure
  • RTL verification; physical design flow
  • Reading a datasheet and a register map as a first instinct

Protocol & Systems Design

  • Perpetual futures — funding rates, margin systems, liquidation engines
  • Lending & borrowing — interest rate models, collateral management, risk parameters
  • Order book architecture on a custom L1 / Avalanche Subnet
  • AMM invariants — StableSwap, CurveCrypto, Newton's method solvers in Solidity

Reliability & Security

  • Audit coordination end-to-end — Dedaub, Sherlock, Code4rena, MoveBit
  • Distributed backends — indexers, liquidator bots, risk monitoring, REST services
  • Retry logic, state management and failure handling for production uptime
  • Instrumentation that proves a claim rather than asserting it

AI Tooling

  • MCP servers exposing execution strategies as agent-callable tools
  • LLM agents for first-pass code review and pre-audit analysis
  • Agent-driven debugging with full protocol context

Writing

Technical deep-dives — robotics control, and earlier work on protocol mathematics.

Education

Indian Institute of Technology (IIT) Delhi
M.Tech — Control & Automation
2017
Indian Institute of Technology (IIT) Jodhpur
B.Tech — Electrical Engineering
2015

Get in Touch

Looking for work in controls, robotics and real-time systems — and open to roles that want both the control-theory side and the production-software side. Based in Goa, India; open to international relocation.

Location:
Goa, India — open to relocation
Twitter:
@0xshinobii