We’re looking for an experienced software engineer excited about joining a small, dynamic startup where you can have a major impact on the company's success. You're passionate about data engineering, distributed systems, and machine learning. You’ll be an essential part of designing and implementing our high performance ML model deployment and inference product, written in Rust. This could mean designing high-performance data connectors, helping develop vertical-specific DSLs, improving the performance of our underlying algorithms, expanding our MLOps capabilities, or rolling client application requirements back into platform features.

Ideally, you have experience writing concurrent code in Rust, Erlang, C/C++, Clojure, or Go. You know your way around both Linux systems-level programming and distributed systems. You understand the pain involved in deploying high-volume data processing systems at scale, and you feel comfortable analyzing and improving system performance. You thrive in an environment where the long-term goals remain stable but day-to-day needs may change quickly. And more than anything, you are committed to continual learning and value sharing your knowledge with the team.


  • Build our machine learning ops platform in Rust
  • Good team communication skills
  • Develop tooling to support development and deployment
  • Work with the delivery team to understand customer requirements and turn them into product features
  • Product sensibility - you can envision the whole system providing real value to customers


Minimum 5 years professional software engineering experience. Specific experience with machine learning and Rust are NOT necessary, but you should be willing to learn fast!

Technical Must-Haves

  • Experience writing concurrent code in Rust, Erlang, C, C++, Clojure, or Go
  • Good Linux operational proficiency
  • Experience writing distributed systems
  • Understanding of deploying high-volume data processing systems at scale

Nice to Have:

  • Prior Rust experience
  • Experience with Linux systems-level programming
  • Experience building Kubernetes containers
  • Experience with one or more cloud environments: AWS, GCP, Azure
  • Experience with MLOps
  • Experience with ONNX, TensorFlow, Keras, or SciKit
  • Analyzing and improving software performance
  • Understanding of software autoscaling

Interview Process

We know interviewing can be a daunting experience, so we would like to provide some visibility into our process.

  • A candidate applies to the position.
  • If the resume meets the requirements described in the job post then Wallaroo schedules an initial screen. This is a one hour interview with 10 questions that help us get a better understanding of your experience and the way that you approach your work. There are no technical questions, and the remainder of the hour (usually about 20 minutes) can be used by the candidate to ask questions.
  • If the initial screen was positive then Wallaroo sends the candidate a description of a technical problem and asks them to program and document a solution. The candidate should spend no more than 3 hours on the solution, but that time can be spread across several days, because we understand that your time is valuable.
  • Once the solution is received Wallaroo will set up a followup interview to discuss the candidate's solution. This interview will be conducted by two members of our engineering team and will focus on correctness, engineering tradeoffs, and exploring alternative solutions.
  • If the technical solution review was positive then the candidate is invited to an interview with the VP of Engineering.
  • If the interview with the VP of Engineering is successful then the candidate is invited to an interview with the CEO of the company.
  • If we feel that the candidate is a good fit for the position based on the interviews then an offer will be made.


Wallaroo is a platform for production AI that helps turn data into business results faster, simpler, and at lower cost. We enable data science teams to get models live against production data three times faster and at 80% lower compute costs, while giving them visibility into how the models are performing, and the power to make quick and easy iterations. Our tech runs as a service inside a client’s environment--whether in a cloud, on-prem, or at the edge--integrating with modern data ecosystems and ML frameworks. Backed by stellar enterprise VC’s, at Wallaroo we believe that we have a $60B market opportunity at the intersection of real-time (streaming) data, AI, and digital transformation.

Engineering Values

  • Distributed First
  • Communication
  • Lifelong Learning
  • Self-motivation and Accountability
  • Respect and Openness
  • Right tool for the Job
  • Adaptability
  • Everyone a Teacher

For details about what these values mean, please see our "Engineering Values" page.

At Wallaroo we believe that the most diverse and inclusive teams build the best products! As an equal opportunity employer, we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.