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# Machine Learning Engineer

- **Company:** Smule, Inc.
- **Location:** United States (Remote available)
- **Contract:** Internship / Graduate position
- **Skills:** Systems Engineering, Automation of Tests, Information Engineering, DevOps, Python (Programming Language), Machine Learning, Open Source Technology, Software Engineering, Feature Engineering, Data Ingestion, Large Language Models, Deep Learning, Backend, Information Technology, Machine Learning Operations, Data Pipelines
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/internship-machine-learning-engineer-smule-company-8337369

## About the Role

* Degree (B.S., M.S., or Ph.D.) in Computer Science, Software Engineering, Electrical Engineering, or a related technical discipline, or currently pursuing one.
 * Strong proficiency in Python and experience with deep learning serving (TorchServe, Triton, vLLM, or equivalent).
 * Solid understanding of systems engineering: networking, storage, containerization, orchestration, and monitoring.
 * Ability to reason about tradeoffs between latency, throughput, cost, and model quality.

Bonus Points For:

 * Experience serving large language models or other generative models at scale.
 * Familiarity with audio/music processing pipelines and real-time inference constraints.
 * Experience with Bayesian optimization, bandit algorithms, or adaptive experimentation platforms.
 * Contributions to open-source ML infrastructure projects.

## Description

We are looking for a Machine Learning Engineer to own the end-to-end lifecycle of ML models in production at Smule, from training and optimization through deployment, monitoring, and iteration. You will work closely with research scientists to bring models off the bench and into scalable, reliable systems that serve millions of users. The ideal candidate is a strong engineer first, with deep practical knowledge of ML systems, a passion for reliability, and an eye for performance.

We strongly encourage candidates with non-traditional backgrounds to apply. If your path into ML engineering came through backend systems, DevOps, audio software, data engineering, or another field, we want to hear from you.

What You'll Be Doing:

 * Design, build, and maintain production ML pipelines encompassing data ingestion, feature engineering, model training, evaluation, and deployment.
 * Optimize models for production constraints including latency, throughput, memory footprint, and cost, using techniques such as quantization, distillation, pruning, and efficient serving architectures.
 * Implement robust monitoring, alerting, and observability for deployed models, covering data drift, prediction quality, and system health.
 * Collaborate with research scientists to integrate new model architectures and training techniques into production systems with minimal friction.
 * Build and improve CI/CD pipelines for ML, including automated testing, validation gates, and staged rollouts.
 * Manage compute infrastructure and costs, making informed tradeoffs between performance, reliability, and budget.


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- [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock)
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