# HPC Engineer at Symbolic Mind

**URL:** <https://discourse.julialang.org/t/hpc-engineer-at-symbolic-mind/131164>\
**Category:** Jobs\
**Created:** [July 29, 2025, 1:08pm UTC](https://discourse.julialang.org/t/hpc-engineer-at-symbolic-mind/131164 "2025-07-29T13:08:05Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![cscherrer](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/cscherrer/32/7631_2.png) [@cscherrer](https://discourse.julialang.org/u/cscherrer)\
**Post date:** [July 29, 2025, 1:08pm UTC](https://discourse.julialang.org/t/hpc-engineer-at-symbolic-mind/131164/1 "2025-07-29T13:08:05Z")

</div>

We’re hiring! No official posting, but this is us:

> **[Symbolic Mind](https://symbolicmind.ai/)**
>
> Next Generation AI Platform.
> Slash AI costs by 100x.

I can’t go into too much detail on our approach, but at a high level we use discrete encoding instead of high-dimensional vectors, and we’re much more CPU focused than typical AI.

We need people to help us scale to new machines with hardware support for hundreds of threads. We’re building a team and value complementary expertise. Strong candidates in any subset of these technical areas are encouraged to apply.

**DM me your CV if you think you’d be a good fit!**  
_UPDATE: Looks like Discourse doesn’t like PDF attachments. You can send to chad ampersand symbolicmind period ai (trying to confuse the scrapers here)_

## **Position Overview**

- Design and optimize massively parallel sequence processing algorithms for NUMA architectures (50+ threads)
- Develop high-performance Julia implementations of string/sequence algorithms at terabyte scale
- Solve memory hierarchy challenges in shared-memory systems with irregular data access patterns
- Build NUMA-aware pipelines for multi-stage sequence processing workflows

## **Core Technical Requirements**

### **NUMA & Parallel Systems**

- **5+ years** scaling algorithms to 50+ core shared-memory systems
- Expert-level understanding of **NUMA topology optimization** and memory controller bottlenecks
- Hands-on experience with **thread affinity management** and CPU pinning strategies
- Proven track record **profiling and resolving** memory bandwidth saturation issues
- Experience with **dynamic load balancing** across NUMA nodes for variable-length workloads

### **Algorithm Optimization**

- Deep expertise in **large-scale string/sequence processing algorithms** (suffix arrays, dynamic programming, graph traversal)
- Experience adapting algorithms for **memory hierarchy constraints** (L3 cache sharing, TLB optimization)
- Background in **lock-free data structures** and work-stealing algorithms for high-throughput systems
- Knowledge of **pipeline parallelization** with stage-wise NUMA affinity management

### **Julia Programming**

- **3+ years** advanced Julia development with focus on high-performance computing
- Expert knowledge of **Julia’s threading model** (`@threads`, `@spawn`, task scheduling)
- Experience optimizing Julia code for **minimal GC pressure** under heavy multithreading
- Understanding of **SIMD utilization** and loop optimization in Julia
- Familiarity with Julia HPC ecosystem (BenchmarkTools.jl, ProfileView.jl, etc.)

## **Desired Experience**

### **Domain Background** (Any of the following)

- **Bioinformatics** : Sequence alignment, genome assembly, phylogenetic algorithms
- **Computational Linguistics** : Large-scale text processing, tokenization, parsing pipelines
- **High-Performance Computing** : Computational physics, scientific computing, or similar scale-intensive domains

### **Performance Engineering**

- Experience with **NUMA-specific profiling tools** (Intel VTune, perf, RAR/MCLPKI analysis)
- Track record of **identifying scaling bottlenecks** beyond 50+ cores
- Knowledge of **memory interleaving vs. locality trade-offs** in practice
- Understanding of **cache performance optimization** for irregular memory access patterns

### **Technical Leadership**

- Experience **architecting systems** that process TB+ datasets efficiently
- Background in **algorithm adaptation** for specific hardware constraints
- Contributions to **open-source HPC projects** or domain-specific libraries
- Experience **mentoring teams** on performance optimization techniques

## **Key Responsibilities**

- Architect and implement **NUMA-optimized sequence processing pipelines**
- Profile and resolve **memory bandwidth bottlenecks** in multi-hundred thread systems
- Design **work distribution strategies** that minimize cross-NUMA synchronization
- Collaborate with domain experts to **translate research algorithms** into production systems
- Lead **performance optimization initiatives** for Julia-based computational workflows
- Develop **reusable libraries** for high-performance sequence processing

## **Technical Assessment Areas**

- **NUMA optimization scenarios** : Memory placement strategies for variable-length sequence data
- **Julia performance debugging** : Identifying and resolving GC pressure and threading bottlenecks
- **Algorithm adaptation** : Modifying sequence algorithms for 50+ thread execution
- **Systems architecture** : Designing pipelines that balance computation and memory bandwidth