Latest Interviews

Teaching in the Age of AI: Learning Goals and the Goals of Learning

Aaron Bauer, Ron

Aaron Bauer transitions from academic research on gamified protein folding and cognitive psychology to a hybrid role at Jane Street that merges software engineering with specialized educator duties. He details the firm's strategy to build an internal ecosystem of OCaml and AI-focused training while addressing technical tooling challenges in VS Code and Emacs through custom integration. Additionally, Bauer outlines how AI has shifted the engineering workflow toward code verification and how Jane Street has restructured its onboarding and internship programs to prioritize active learning, testing, and design over passive code generation.

Wrestling the World Into Rows with Eric Mannes

Eric Mannes, Ron

Eric Manis details Jane Street's evolution from a trading firm into a technology-driven organization by shifting from centralized infrastructure to embedded "desk devs" and launching a scaled alternative data team. The firm navigates complex market mechanics, such as the 2020 negative oil pricing event, by prioritizing rigorous data engineering and physical world insight over pure mathematical modeling. This strategic focus on high-quality unstructured data and specialized hiring practices has enabled the company to transform raw information into actionable trading signals while maintaining robust risk management systems.

Positional Encodings and Group Theory | 3Blue1Brown and Alok Puranik

3Blue1Brown, Alok Puranik, Grant Sanderson

This theoretical framework establishes that valid positional encodings in attention mechanisms are mathematically defined by a minimal set of linearity and translation variance assumptions, resulting in a general form where the transformation matrix is the exponential of a constant matrix. By analyzing the eigenvalues of this matrix, the work classifies existing methods into distinct dynamic behaviors, explicitly identifying Rotary Positional Embeddings (RoPE) as a pure rotation case while recovering ALiBi as a non-diagonalizable construction yielding linear dependence. The analysis concludes that the space of stable, useful encodings is effectively exhausted by combinations of exponential decay, pure rotation, and damped rotation, suggesting that future novel approaches would likely reside in unstable or higher-order polynomial regimes.

3Blue1Brown Talks Machine Learning with Jane Street

Jane, Grant Sanderson, Nitya, In Young, Craig, Alok

Jane Street operates as a generalist financial liquidity provider that distinguishes itself through a culture prioritizing collaborative truth-seeking, Bayesian belief updating, and a real-time auction system for compute resource allocation. The firm fundamentally transforms its trading approach by deploying advanced machine learning and neural networks to solve complex prediction problems, contrasting sharply with the linear models and manual order entry typical of traditional financial institutions. This unique environment, defined by low hierarchy and high agency, attracts adaptable generalists and supports deep academic research into fields ranging from functional programming to statistical physics, resulting in high retention and significant industry innovation.

A Look Inside Jane Street’s Internship Program 2026

A candidate from an aerospace and computer science background secured an internship at Jane Street by solving a puzzle and demonstrating a collaborative problem-solving approach during interviews. During the role, the intern built a tool for bond desks and optimized GPU memory management using autoencoders, with the resulting code now deployed in active training systems. The experience highlighted a firm culture that values learning from mistakes and adaptability, featuring mentorship in a commodities trading project and social connections through shared hobbies like the Irish fiddle.

8 Figures on a Design Doc

Jacob Baskin

Before launching its "Superstore" system, Jane Street authorized a small team to purchase 10 petabytes of storage appliances for approximately $80 million based solely on a design document. This rapid, high-stakes capital expenditure of eight figures, which starkly contrasts with the centralized allocation processes the decision-maker previously encountered at Google, highlights the firm's unique operational agility. The speaker describes this unprecedented scale of immediate spending as both impressive and nervously significant for an organization of its size.

Building a Data Warehouse From Scratch with Jacob Baskin

Jacob Baskin, Ron

Jacob Baskin's career spans mechanism design research at Brown, building real-time ad exchanges at Google, and attempting to digitize urban parking infrastructure. At Jane Street, he engineered the SuperStore distributed database to replace legacy monoliths and designed the auction-based resource allocation system for the massive Hive compute cluster. His current work focuses on refining these mechanisms by addressing utility gaps in bidding models and implementing scalable data distribution architectures to support large-scale machine learning and simulation tasks.

The Network as a Program with Nate Foster

Nate Foster, Ron Minsky

Nate Foster, a professor at EPFL and visiting researcher at Jane Street, bridges programming languages and networking by advocating for treating networks as verifiable programs through formal methods and Domain Specific Languages like Netcat and P4. His work has evolved from foundational theories on bidirectional data conversion to practical industrial applications, including the Butane system which compiles high-level latency policies into safe, distributed BGP configurations to replace error-prone manual router management. Foster's approach combines algebraic analysis of routing semantics with a software engineering culture that prioritizes rigorous testing and visualization, enabling complex network modifications while challenging traditional end-to-end principles in favor of in-network computing for specialized workloads.

Jane Street on GPUs, Trading, and Hiring: A Conversation with Dwarkesh

Jane Street, Dwarkesh Patel, Ron Minsky, Dan Pontecorvo

Jane Street executes a multi-tiered trading strategy that balances nanosecond-scale FPGA execution with long-horizon AI models while transitioning to a disaggregated global network featuring a $6 billion compute partnership with CoreWeave. The firm prioritizes model architecture diversity and custom hardware, including ARM and NVIDIA infrastructures, to navigate high-noise financial data without relying on single foundation models. This approach is supported by a hiring strategy focused on scarcity-driven talent acquisition and a human-in-the-loop philosophy that maintains critical judgment during complex market anomalies.

Dwarkesh Goes Inside Jane Street's Latest AI Data Center

Dwarkesh, Ron Minsky, Daniel Pontecorvo, Mark Mirchandani

Jane Street transformed a Texas data center into a high-density liquid-cooled facility housing 4,032 GPUs to execute large language model training and custom trading architectures. The retrofit replaces legacy air-cooling with an 18°C fluid distribution system that manages 140 kW per cabinet while utilizing proprietary software to dynamically redistribute power and prevent breaker trips during peak loads. Engineering safeguards now focus on mitigating new liquid-cooling risks like biological growth and leaks, enabling sub-100-nanosecond latency required for modern algorithmic trading compared to the millisecond scales of its historical "Hive" cluster.

Why Testing Is Hard and How to Fix It with Will Wilson

Will Wilson, Ron Minsky

Antithesis CEO Will Wilson founded the company to solve the non-determinism and state-space explosion challenges that plague traditional testing in complex distributed systems. The platform utilizes hypervisor-level emulation to create deterministic simulations of unmodified software, enabling exhaustive state-space exploration without the need for extensive manual test definitions. This approach has attracted major institutional users like Jane Street, which serves as both a customer and a Series A investor to validate the tool's efficacy in finding critical concurrency bugs and accelerating AI-driven development cycles.

Why ML Needs a New Programming Language with Chris Lattner

Chris Lattner, Ron Minsky

Former LLVM creator Chris Lattner founded Modular to solve the AI industry's fragmentation by introducing Mojo, a Python-compatible language designed to unify performance and portability across competing hardware vendors. The company generates revenue through enterprise infrastructure solutions rather than licensing the language itself, aiming to replace complex C++ and CUDA workflows with a single release train supporting both NVIDIA and AMD accelerators. This approach seeks to democratize high-performance computing by providing a neutral software foundation that allows developers to build portable AI models without sacrificing hardware efficiency.

The Thermodynamics of Trading with Daniel Pontecorvo

Daniel Pontecorvo, Ron Minsky, Dan Pontecorvo, Mark Mirchandani, David Abelson

Jane Street's physical engineering team manages a complex lifecycle of data centers and offices, prioritizing microsecond-level latency constraints and evolving from traditional air cooling to high-density liquid immersion solutions for AI infrastructure. The organization deploys proprietary monitoring stacks to prevent catastrophic failures while optimizing Power Utilization Efficiency through economizer cycles and rear-door heat exchangers that handle rack densities up to 170 kW. Their human-centric workspace design leverages modular furniture and circadian lighting to sustain collaboration, supported by a hiring strategy that blends industry veterans with graduates to challenge established engineering assumptions.

The Saga of Multicore OCaml

After a nine-year development cycle, Jane Street successfully deployed Runtime 5, a multi-core garbage collector for OCaml that addresses initial performance regressions through a redesigned pacing model and a Mark Delay synchronization patch. The team resolved complex "load-bearing bugs" and floating garbage issues to achieve stable parallelism while maintaining the sequential performance required for high-frequency trading environments. Despite rejecting the ownership model of languages like Rust, Jane Street is now releasing its production-ready OxCamel branch to facilitate researcher experimentation on future parallelism tools and type-level race freedom guarantees.

Building Tools for Traders with Ian Henry

Ian Henry, Ron Minsky, Mark Mandelbaum, Mark Mirchandani, David Eastman, Brian Dorsey

Ian Henry, a Jane Street engineer who transitioned from general web development to options trading tools, leverages the firm's custom OCaml ecosystem to build high-density interfaces that eliminate mouse usage and manage complex volatility surfaces. His recent work includes creating Bauble, a live 3D graphics environment in the Janet language that demonstrates how side projects using image-based state serialization and novel macro systems can rapidly inform internal production tooling. Through this process, Henry illustrates how Jane Street bridges the gap between low-latency exchange architecture and expert user needs by prioritizing type safety, extreme information density, and a parallel software universe free from standard web protocols.