Listen in on Jane Street’s Ron Minsky as he has conversations with engineers who are working on everything from clock synchronization to reliable multicast, build systems to reconfigurable hardware. Get a peek at how Jane Street approaches problems, and how those ideas relate to tech more broadly. You can find transcripts along with related links on our website at signalsandthreads.com.

Ian Henry started his career at Warby Parker and Trello, building consumer apps for millions of users. Now he writes high-performance tools for a small set of experts on Jane Street’s options desk. In this episode, Ron and Ian explore what it’s like writing code at a company that has been “on its own parallel universe software adventure for the last twenty years.” Along the way, they go on a tour of Ian’s whimsical and sophisticated side projects—like Bauble, a playground for rendering trippy 3D shapes using signed distance functions—that have gone on to inform his work: writing typesafe frontend code for users who measure time in microseconds and prefer their UIs to be “six pixels high.”You can find the transcript for this episode on our <a href="https://signalsandthreads.com/building-tools-for-traders" target="_blank">website</a>.Some links to topics that came up in the discussion:<ul><li><a href="https://bauble.studio/">Bauble studio</a></li><li><a href="https://janet.guide/">Janet for Mortals, by Ian Henry</a></li><li><a href="https://blog.janestreet.com/the-joy-of-expect-tests/">What if writing tests was a joyful experience?</a> </li></ul>

Signals and Threads

Building Tools for Traders with Ian Henry

In Young Cho thought she was going to be a doctor but fell into a trading internship at Jane Street. Now she helps lead the research group’s efforts in machine learning. In this episode, In Young and Ron touch on the porous boundaries between trading, research, and software engineering, which require different sensibilities but are often blended in a single person. They discuss the tension between flexible research tools and robust production systems; the challenges of ML in a low-data, high-noise environment subject to frequent regime changes; and the shift from simple linear models to deep neural networks.You can find the transcript for this episode on our <a href="https://signalsandthreads.com//finding-signal-in-the-noise/" target="_blank">website</a>.

Finding Signal in the Noise with In Young Cho

Sylvain Gugger is a former math teacher who fell into machine learning via a MOOC and became an expert in the low-level performance details of neural networks. He’s now on the ML infrastructure team at Jane Street, where he helps traders speed up their models. In this episode, Sylvain and Ron go deep on learning rate schedules; the subtle performance bugs PyTorch lets you write; how to keep a hungry GPU well-fed; and lots more, including the foremost importance of reproducibility in training runs. They also discuss some of the unique challenges of doing ML in the world of trading, like the unusual size and shape of market data and the need to do inference at shockingly low latencies.You can find the transcript for this episode  on our <a href="https://signalsandthreads.com/the-uncertain-art-of-accelerating-ml-models" target="_blank">website</a>.Some links to topics that came up in the discussion:<ul><li><a href="https://course.fast.ai/">“Practical Deep Learning for Coders,”</a> a FastAI MOOC by Jeremy Howard, and the <a href="https://fastai.github.io/fastbook2e/">book</a>, of which Sylvain is a co-author.</li><li>The <a href="https://dawn.cs.stanford.edu/benchmark/#cifar10-train-cost">Stanford DAWNBench</a> competition that Sylvain participated in.</li><li><a href="https://huggingface.co/">HuggingFace</a>, and the <a href="https://huggingface.co/docs/accelerate/en/index">Accelerate library</a> that Sylvain wrote there.</li><li>Some of the languages/systems for expression ML models that were discussed: <a href="https://pytorch.org/">PyTorch</a>, <a href="https://www.tensorflow.org/">TensorFlow</a>, <a href="https://github.com/jax-ml/jax">Jax</a>, <a href="https://www.modular.com/mojo">Mojo</a>, and <a href="https://triton-lang.org/main/index.html">Triton</a></li><li>CUDA <a href="https://developer.nvidia.com/blog/cuda-graphs/">graphs</a> and <a href="https://developer.download.nvidia.com/CUDA/training/StreamsAndConcurrencyWebinar.pdf">streams</a></li><li><a href="https://people.eecs.berkeley.edu/~brecht/papers/hogwildTR.pdf">Hogwild concurrency</a></li></ul>

The Uncertain Art of Accelerating ML Models with Sylvain Gugger

Liora Friedberg is a Production Engineer at Jane Street with a background in economics and computer science. In this episode, Liora and Ron discuss how production engineering blends high-stakes puzzle solving with thoughtful software engineering, as the people doing support build tools to make that support less necessary. They also discuss how Jane Street uses both tabletop simulation and hands-on exercises to train Production Engineers; what skills effective Production Engineers have in common; and how to create a culture where people aren’t blamed for making costly mistakes.You can find the transcript for this episode  on our <a href="https://signalsandthreads.com/solving-puzzles-in-production" target="_blank">website</a>.Some links to topics that came up in the discussion:<ul><li>More about <a href="https://www.janestreet.com/production-engineering/">production engineering</a> at Jane Street, including how to apply.</li><li>Notes on <a href="https://en.wikipedia.org/wiki/Site_reliability_engineering">Site reliability engineering</a> in the wider world.</li><li><a href="https://en.wikipedia.org/wiki/Alarm_fatigue">Alarm fatigue</a> and desensitization.</li><li>Jane Street’s <a href="https://en.wikipedia.org/wiki/S-expression">1950’s era serialization-format of choice</a>,</li><li><a href="https://keeptalkinggame.com/">Some</a> <a href="https://store.steampowered.com/app/448510/Overcooked/">games</a> that Streeters have used for training people to respond to incidents.</li></ul>

Jane Street

Signals and Threads by Jane Street

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