Asks for

The ad says“Experience with active learning, auto-labeling, or human-in-the-loop ML systems”
The ad says“Experience with active learning, auto-labeling, or human-in-the-loop ML systems”
The ad says“Background in autonomous driving or robotics perception”
The ad says“Familiarity with modern ML infrastructure, data-centric AI approaches and running large-scale jobs on GPUs”
The ad says“Created or worked on microservices and/or databases for data-oriented software”
The ad says“Experience with Docker, Kubernetes, Opensearch and Postgres”
The ad says“Full-stack experience React, TypeScript Python, Golang or similar”
The ad says“Familiarity with modern ML infrastructure, data-centric AI approaches and running large-scale jobs on GPUs”
The ad says“Experience with active learning, auto-labeling, or human-in-the-loop ML systems”
The ad says“Experience with Docker, Kubernetes, Opensearch and Postgres”
The ad says“Direct experience with foundation models, including LLMs and VLMs, for data automation tasks”
The ad says“Created or worked on microservices and/or databases for data-oriented software”
The ad says“Familiarity with modern ML infrastructure, data-centric AI approaches and running large-scale jobs on GPUs”
The ad says“Experience with Docker, Kubernetes, Opensearch and Postgres”
The ad says“Experience with Docker, Kubernetes, Opensearch and Postgres”
The ad says“Full-stack experience React, TypeScript Python, Golang or similar”
The ad says“Full-stack experience React, TypeScript Python, Golang or similar”
The ad says“Background in autonomous driving or robotics perception”
The ad says“Full-stack experience React, TypeScript Python, Golang or similar”
The ad says“Direct experience with foundation models, including LLMs and VLMs, for data automation tasks”

The Axion Data team at Applied Intuition is building the data engine to train perception models
We build the means to run models at the edge such as Automatic Target Recognition (ATR) models, then backhaul data to our cloud pipeline that ingests and manages this data to enable the continual improvement of these models
As a data engineer experienced in machine learning, you will be responsible for both our edge application which runs perception stacks and our cloud data engine which ingests video data to run the next iteration of the model before we deploy it back down to the edge
This includes optimizing our ability to run these models on both the edge and the cloud, as well as building MLOps tooling to gain insight and visibility into the pipeline for our stakeholders
Construct optimized data pipelines to run ML models
Evolve our data engine architecture to scale high-fidelity labels, reduce annotation costs, and accelerate ML iteration cycles
Integrate foundation models (LLMs, VLMs, and multimodal models) to automate and enhance labeling, quality assurance, and data discovery
Leverage software-in-the-loop and hardware-in-the-loop testing
Interact with the DoD customer to understand their use cases, requirements, and triage needs during field events to deliver a superior customer experience

Preferred experience

Created or worked on microservices and/or databases for data-oriented software
5+ years of relevant work experience
A hunger to learn and grow into a position of ownership and impact on a new product team
Familiarity with modern ML infrastructure, data-centric AI approaches and running large-scale jobs on GPUs
U.S. citizenship (legally required) and eligibility to obtain a security clearance
Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles
Experience with active learning, auto-labeling, or human-in-the-loop ML systems
Full-stack experience React, TypeScript Python, Golang or similar
Experience with Docker, Kubernetes, Opensearch and Postgres
Background in autonomous driving or robotics perception
Direct experience with foundation models, including LLMs and VLMs, for data automation tasks