Strange Loop Labs Logo

Strange Loop Labs

Engineer

Reposted One Month Ago
Remote
Hiring Remotely in United States
Mid level
Remote
Hiring Remotely in United States
Mid level
As an AI Engineer, you'll work closely with customers to understand their needs, train and deploy models, and build scalable AI systems.
The summary above was generated by AI

Strange Loop automates operations at the world’s largest financial institutions with specialized AI agents.

We are a team of engineers (and only engineers) who own both the customer relationship and product end-to-end. Our goal is to remove as many layers as possible between the customer, the engineer, and the product. We don’t have salespeople, product managers, or layers of management, nor do we divide engineers into platform/ML/FDE silos. Our average week involves:

  • Mastering our customers’ processes as well as their associates

  • Building AI agents that automate it

  • Carrying what we learn in the field back into a robust, scalable cloud platform

This model is not right for many exceptional engineers, nor should most companies adopt it. But if this sounds like the kind of company you want to build, we’d love to hear from you.

In addition to your resume, please include a 1-2 page essay as a cover letter on which principle from the essays below resonates most deeply with you, and provide at least one example from your career of when you have applied that principle (or a time when you wished you had applied it):

Our Company Principles

As focused as we are on building great solutions for our customers, we are just as focused on building a great company. We have learnt from the good, the bad, and the ugly that our industry has to offer, and are using this to create the company we always wanted to work for.

 

Stay small

As anyone who has worked at a large organization knows, keeping hundreds or thousands of people aligned, communicating, and working effectively is difficult, if not impossible. More people, more problems.

At Strange Loop, we choose to stay small. This means everyone knows each other, is aligned on strategy, and can re-use the cool stuff others have built. We are energized by figuring out how to get more out of our small team.

No middle

Staying small requires us to remove a lot of the waste that exists at larger companies. One of those things is the expansive tier of project, product, and middle managers required to keep the behemoth running.

At Strange Loop, Engineers speak directly with customers. This enables them to build better products and work around deadlines. Engineers feel the pain of their users, and the relief when they solve it.

Remote first

Talented people deserve to work where they want. They will not do better work if they live in a particular part of the world, or with a manager looking over their shoulder.

Remote work introduces a number of challenges, just as in-office does. We are committed to continually finding better ways of solving them.

Share the wealth

At Strange Loop, every employee receives salary and equity. We want employees to benefit if we do exit, but we don't want an exit to become the goal. Good companies focus on solving customer problems long-term, not optimizing revenue short-term.

To facilitate this, we share the profits we make with all our employees annually. Everyone receives an equal share. This means everyone benefits from the good work they do, and is incentivized to build a better company for the long-term.

Our Engineering Principles

These are a set of principles that guide how we build software at Strange Loop. You will see examples of these all throughout our codebase, and in the way we work with customers. We reserve the right to change them as we find better ways of working.

Always Be Learning

Software and customers are changing constantly, as technologies improve and markets change. Therefore, the only sustainable competitive advantage is the ability to learn.

We continually discover better ways to leverage technology and serve customers. We also continually learn about how our customers work and the challenges they face, so that we can build them better solutions.

Build Quality In

We define a high-quality product as one that works the way customers expect it to and is easy to modify in the future. Adding quality in after a product has been released can be difficult: customers will be using it and finding bugs, which creates support load that steals time away from everything else. Therefore, we build quality in from the start, even if doing so means that a feature takes a little longer to build. The time is paid back quickly.

Always Be Shipping

When we’re building a product, we make a long list of assumptions about how we think customers will use it. Naturally, some of those assumptions are wrong, but we only find out which ones when our product is in the hands of real customers. We therefore try to ship small increments of a product sooner to customers, and seek their feedback as quickly as possible.

Make Incremental Improvements

Our products are never perfect. There is an infinite list of features we could add, and bugs we could fix, and latencies we could optimize to make them better. Unfortunately, though, we don’t have an infinite amount of time to do them all!

Occasionally we will schedule time to make a large improvement, but most of the time we focus on making small, incremental improvements as we go. These small improvements compound over time: each one makes the system easier to develop and operate, which frees up more time, which allows more improvements to be made.

Fail Better Next Time

We will fail sometimes. Our services will go down, or our accuracy will be poor, or our features will be too difficult to use. This is a natural part of product development. When it happens, no blame will be apportioned, because everyone involved was already trying to do their best. Instead, in these moments of failure, our focus is on what we can gain from it. We dissect what went wrong, possibly come up with some action items, but above all else learn.

Create Leverage

We deeply embed our engineers with customers, so that we can understand their problems inside and out. Unlike a consulting firm though, we have no intention of hiring thousands of engineers to scale. This means we find ways to create more with less. We do this by building levers into every system we touch so that we can move mountains.

Think About The System

The success of our company is the result of a large, interconnected, network of “things”. There are customers, and engineers, and a CEO, and a CTO, and services, and databases, and machine-learned models, and public clouds, and many more things.

All of these interact in unpredictable ways to create a complex system. When we’re doing our work, though, we only operate on one tiny part at a time. This makes it hard to see the forest for the trees. When making decisions, we do our best to think at the level of the system, and do things that will benefit it as a whole.

Similar Jobs

2 Hours Ago
In-Office or Remote
73K-130K Annually
Mid level
73K-130K Annually
Mid level
Artificial Intelligence • Big Data • Healthtech • Information Technology • Machine Learning • Software • Analytics
Develop, test, deploy, and maintain software applications, including legacy MCDS systems and next-generation CCS 2.0 solutions. Collaborate with architects, product managers, and engineering teams on scalable healthcare technologies. Responsibilities include application troubleshooting, performance optimization, code reviews, documentation, quality assurance, architectural design, and use of approved AI tools to improve development efficiency.
Top Skills: Asp.NetAWSAzureC#Ci/CdDockerGCPGitKubernetesMongoDBMySQLOraclePostgresPythonRestful ApisTypescript
2 Hours Ago
In-Office or Remote
92K-164K Annually
Senior level
92K-164K Annually
Senior level
Artificial Intelligence • Big Data • Healthtech • Information Technology • Machine Learning • Software • Analytics
Senior Data Engineer responsible for Epic and EHR integrations supporting value-based care, risk and quality workflows, member attribution, roster management, and care-gap tools. The role designs and executes EHR development tasks, coordinates with clinical, data, operations, and development teams, documents business and data flows, evaluates AI and automation opportunities, and develops reporting to identify data-quality issues and prevent outages.
Top Skills: Ai ToolsCaboodleClarityEhr IntegrationsEpicHealthy PlanetSQL
2 Hours Ago
In-Office or Remote
146K-250K Annually
Senior level
146K-250K Annually
Senior level
Artificial Intelligence • Big Data • Healthtech • Information Technology • Machine Learning • Software • Analytics
Leads the design, development, deployment, and scaling of production AI/ML solutions for healthcare. Builds cloud-based machine learning systems, data pipelines, feature workflows, and MLOps processes including CI/CD, monitoring, drift detection, retraining, and governance. Provides architecture guidance, technical leadership, mentorship, and cross-functional partnership while applying NLP, deep learning, computer vision, foundation models, and other advanced AI techniques.
Top Skills: AWSAzureAzure Machine LearningCi/CdCloud ComputingComputer VisionData PipelinesDatabricksDeep LearningDistributed ComputingFoundation ModelsGCPLarge Language ModelsMachine LearningMlopsNlpPysparkPythonSnowflakeSQL

What you need to know about the Austin Tech Scene

Austin has a diverse and thriving tech ecosystem thanks to home-grown companies like Dell and major campuses for IBM, AMD and Apple. The state’s flagship university, the University of Texas at Austin, is known for its engineering school, and the city is known for its annual South by Southwest tech and media conference. Austin’s tech scene spans many verticals, but it’s particularly known for hardware, including semiconductors, as well as AI, biotechnology and cloud computing. And its food and music scene, low taxes and favorable climate has made the city a destination for tech workers from across the country.

Key Facts About Austin Tech

  • Number of Tech Workers: 180,500; 13.7% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Dell, IBM, AMD, Apple, Alphabet
  • Key Industries: Artificial intelligence, hardware, cloud computing, software, healthtech
  • Funding Landscape: $4.5 billion in VC funding in 2024 (Pitchbook)
  • Notable Investors: Live Oak Ventures, Austin Ventures, Hinge Capital, Gigafund, KdT Ventures, Next Coast Ventures, Silverton Partners
  • Research Centers and Universities: University of Texas, Southwestern University, Texas State University, Center for Complex Quantum Systems, Oden Institute for Computational Engineering and Sciences, Texas Advanced Computing Center

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account