Software Engineer, Machine Learning Infrastructure

Botauto; Houston (TX); On-site; USD 90,000 - 120,000; Full time

Job summary

Botauto in Houston is seeking a skilled Software Engineer to design and develop machine learning infrastructure central to autonomous trucks. The role involves building evaluation platforms, training workflows, and data annotation systems.

The ideal candidate will have a strong background in Python and C++, alongside experience in distributed systems. Join us in transforming transportation with innovative solutions.

Qualifications

• Bachelor's or Master's in Computer Science, or equivalent practical experience.
• Strong proficiency in Python; working knowledge of C++.
• Demonstrated hands-on experience building or scaling evaluation platforms.

Responsibilities

• Architect and own a scalable, end-to-end model evaluation platform.
• Partner with research scientists to optimize and scale distributed training workflows.
• Design and maintain a versioned, high-quality training data store.

Skills

Python

C++

Kubernetes

Spark

Education

Bachelor's or Master's in Computer Science

Tools

MLFlow

WandB

ClearML

Job description

Company Introduction

At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.

We are seeking a highly skilled and motivated Software Engineer to design, develop, and scale our machine learning annotation, evaluation, and training infrastructure. This role is central to the quality and velocity of our perception and ML models — from curating and managing high-quality annotated datasets, to building robust evaluation pipelines that drive continuous model improvement. The ideal candidate combines strong systems engineering skills with a deep understanding of ML Workflows/Ops and large-scale data infrastructure.

Key Responsibilities

Machine Learning & Deep Learning Infrastructure

• Evaluation Platform — Architect and own a scalable, end-to-end model evaluation platform for perception and prediction models central to autonomous driving. Define metrics, design for scale, and make results actionable for researchers.
• Training Infrastructure — Partner with research scientists to optimize and scale distributed training workflows. Integrate experiment tracking and reproducibility into the model lifecycle from day one.
• Dataset & Feature Store — Design and maintain a versioned, high-quality training data store that accelerates model development and supports rapid iteration.
• ML Pipelines — Build automated pipelines spanning data preparation, model training, validation, and deployment — enabling fast experimentation and reproducible outcomes.
• Annotation Platform — Contribute to tooling and infrastructure that powers high-throughput, high-accuracy data annotation at scale.
• MLOps — Develop production ML services that treat models as products — with reliability, observability, and continuous improvement built in.

Data Infrastructure

• Maintain and evolve a robust data storage and access layer (S3 data lake, Delta Lake) underpinning annotation, evaluation, and training workflows.
• Build scalable, reliable data collection pipelines supporting diverse vehicle dispatch missions.
• Develop foundational services and packages that provide clean, performant access to autonomous driving data across the stack.

Qualifications

Required:

• Educational Background: Bachelor's or Master's in Computer Science, or equivalent practical experience.
• Strong Programming Skills: Strong proficiency in Python; working knowledge of C++
• ML/DL Infrastructure Experience — Demonstrated hands-on experience building or scaling at least one of the following in a production environment:
  • Evaluation platforms — automated model benchmarking, metric computation, and regression tracking across model versions.
  • Training infrastructure — distributed training pipelines, experiment tracking, and model lifecycle management (e.g. W&B, MLflow, ClearML).
  • Dataset curation & feature stores — versioned dataset management, data lineage, and tooling for high-quality training data at scale.
  • Annotation platforms — tooling or pipelines that support high-throughput, high-accuracy labeling workflows.
• Distributed Systems — Strong experience with distributed computing and container orchestration — Kubernetes, Spark, or comparable frameworks.
• Ability to operate independently: scope ambiguous problems, make sound architecture decisions, and drive them to completion.

Preferred:

• C++ experience in performance-sensitive or safety-critical applications
• Full-stack service development experience.
• Prior work in autonomous driving or robotics.