Spring Internship 2026At — Locus Technologies, Inc.

We are looking for a Software Engineer intern to join the Locus AI/ML Initiatives Team. This role offers an exciting opportunity to work on various aspects of the software development project end-to-end. It includes building and improving our streaming data infrastructure and developing machine learning models on top of the data collected. This internship offers hands-on experience with real-world data pipelines, scalable systems, and applied machine learning, working closely with experienced engineers and data scientists.

The ideal candidate will have a strong foundation in data structures, machine learning, and will be responsible for streamlining data pipelines, evaluating and deploying ML models.

Tasks:

a. Design, build, and maintain streaming data pipelines (e.g., ingestion, processing, storage)
b. Work with real-time data frameworks and messaging systems
c. Clean, transform, and analyze large-scale streaming and batch datasets
d. Develop, train, and evaluate machine learning models using collected data
e. Deploy and monitor ML models and data pipelines in development or production environments
f. Collaborate with cross-functional teams to translate business or product needs into technical solutions
g. Document designs, experiments, and results

Training:

a. Software Development Lifecycle processes that include: 1) Understanding the Agile sprint process that includes stand-up calls 3 times/week, 2) Participating in the Code Review process.
b. Working and collaborating with senior developers, QA engineers, product managers and database administrators.
c. Weekly meetings to review progress and provide feedback and mentorship.
d. Presentations to a larger team and demo-ing the product features to gather feedback and improvise.

Learning Outcome:

a. Gain experience with streaming technologies (e.g., Kafka, Flink, Spark Streaming, Kinesis)
b. Gain experience with ML libraries/frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
c. Hands-on experience building scalable streaming data systems
d. Practical experience applying machine learning to real-world problems, specifically in cloud-based applications.
e. Mentorship from experienced engineers and data scientists
f. Exposure to production-grade infrastructure and ML workflows