ML Data Infra Engineer - Build Autonomous Data Pipelines

HavocAI; United States; On-site; USD 120,000 - 190,000; Full time

Benefits offered by this job

Health, Dental and Vision Insurance (U

Life Insurance (Employer Paid)

401k Matching

Unlimited PTO with 2 weeks minimum

Equity Package

Work/Home Office Stipend

Global Entry

Parental Leave (12 weeks)

Job summary

HavocAI seeks a Data & ML Infrastructure Engineer to build data pipelines and a video/telemetry data lake for autonomous systems. You will own ingestion, storage, indexing, metadata, and dataset generation to support ML training, evaluation, and debugging.

Join a hands-on role focused on turning messy real-world data into reliable tooling, with opportunities to collaborate with Autonomy, Perception, and Software teams across multiple data modalities.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, ML, or a related field.
• 3+ years of data engineering, ML infrastructure, or related roles.
• Experience designing and operating production data pipelines for large-scale data.
• Experience with video, imagery, telemetry, sensor data, or other high-volume data.

Responsibilities

• Build and maintain data infrastructure for multi-modal data assets and a video/telemetry data lake.
• Own ingestion, storage, indexing, metadata, and lifecycle management of data.
• Develop scalable pipelines to curate datasets for ML training and evaluation.
• Create tools for data search, filtering, tagging, and retrieval across platforms.
• Collaborate with Autonomy, Perception, Software, and Field Operations teams.

Skills

Python

SQL

Data engineering

ML infrastructure

Backend systems

MLOps

Diagnostics and observability

Independent ownership

Education

Bachelor’s degree in Computer Science or related field

Tools

S3-compatible storage

PostgreSQL

Spark

Ray

Airflow

Dagster

Kubernetes

Docker

Kafka

Job description

HavocAI seeks a Data & ML Infrastructure Engineer to build data pipelines and a video/telemetry data lake for autonomous systems. You will own ingestion, storage, indexing, metadata, and dataset generation to support ML training, evaluation, and debugging.

Join a hands-on role focused on turning messy real-world data into reliable tooling, with opportunities to collaborate with Autonomy, Perception, and Software teams across multiple data modalities.