AI/ML Data Engineer
AI/ML Data Engineer
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Job Description
Job description
is an earlystage startup at the forefront of agentic AI for operations functions. We are building a new category called the Autonomous Workforce Platform.
An Autonomous Workforce Platform delivers fully operational digital employees that perform entire job roles not just tasks with no hiring HR burden or human limitations.
We are building a world where every business can instantly scale its workforce without hiring overhead or human limitations by delivering digital employees who work faster scale instantly and operate 24/7.
Were a highly collaborative onsite team based in the heart of downtown Austin. Our founding team has a proven track record of taking SaaS businesses from 0 to 1 and were committed to solving hard problems building straightforward products and fostering a culture of trust ownership and optimism.
Why Join Us
Be part of a foundational team building a product from the ground up
Solve meaningful problems that impact real industries and real people
Work closely with experienced operators and engineers whove done this before
Enjoy a collaborative inperson culture in a vibrant Austin office
Key Responsibilities:
Design build and maintain robust data pipelines for ingestion transformation and storage of structured and unstructured data.
Collaborate with AI/ML teams to understand data requirements for model training finetuning inference in order to deploy AI/LLMpowered solutions to automate complex workflows.
Implement scalable ETL processes to handle large volumes of data across multiple modalities (e.g. text images audio).
Work with data labeling teams and platforms to create highquality annotated datasets.
Build and maintain feature stores data lakes and data warehouses optimized for AI use cases.
Collaborate crossfunctionally to ensure engineering solutions align with business goals and ensure data lineage governance and quality across all stages of the data lifecycle.
Optimize data flows for performance cost and latency in both batch and realtime environments.
Navigate and integrate with legacy systems and ensure compliance with regulatory standards also partner with DevOps/ML Ops to support model deployment pipelines and data monitoring infrastructure.
Job requirements
What Were Looking For:
Startup Mindset & Initiative: Selfstarter who thrives in fastpaced ambiguous environments. Comfortable taking ownership and driving results without needing stepbystep instructions.
Technical Excellence: 3 years of experience in data engineering data infrastructure or ML data pipelines. Experienced dealing with structured semistructured and unstructured data types. Strong CS engineering fundamentals and sound decisionmaking. Proficient in Python and SQL with solid experience in distributed data processing such as (Apache Spark Kafka Airflow DBT).
Strong Background in Data Science: Handson ETL and ML Ops implementation. Experience building ETL pipelines feature engineering. Experience working with cloud platforms (AWS GCP or Azure) and data services such as S3 or BigQuery.
FullStack Development Skills: Experience with REST APIs web frameworks and relational databases. Familiarity with ML workflows and AI frameworks (e.g. TensorFlow PyTorch Hugging Face MLflow). Nice to know our core tech stack is a plus: NodeJS / NestJS Prisma PostgreSQL OpenAI / Gemini Google Cloud Platform (GCP)
Collaborative Spirit: Strong communication skills and a teamoriented mindset. Able to brainstorm problemsolve and iterate with colleagues across functions.
All done!
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Employment Type
Full-Time
