Lead Data Platform Engineer ( AI/ML , Start-up)

TEEMA Solutions Group; Toronto; On-site; CAD 100,000 - 130,000; Full time

Job summary

A technology startup is seeking a Data Platform Software Lead Engineer to drive the architecture underlying its AI/ML pipelines. In this role, you'll design and build reliable systems for data ingestion and processing, focusing on large-scale code and text datasets. The ideal candidate has over 8 years of experience in data-intensive engineering and skills in Python, Spark, and AWS. Join a diverse team and influence the technical path of an innovative company.

Benefits

Competitive pay with equity

Work with cutting-edge cloud and ML/AI technologies

Collaborate with a diverse, high-caliber team

Dynamic, innovation-driven workplace culture

Qualifications

• 8+ years in data-intensive software engineering.
• Proficiency in Python, Go, or Scala; Spark or Ray; Airflow or Prefect; Kafka; Redis; Postgres or ClickHouse; GitHub APIs.
• Understanding of how datasets power AI/ML workflows.
• Proven experience in scalable data infrastructure and pipeline development.
• Skills in web crawling, scraping, and large-scale ingestion.
• Cloud-native experience (e.g., AWS, containerized compute, security).

Responsibilities

• Architect and implement scalable data platforms for code/text dataset ingestion, processing, and delivery.
• Build web-scale crawling and metadata extraction tools from open-source code repositories.
• Develop reliable, distributed pipelines with frameworks like Spark, Kafka, and Airflow/Prefect.
• Enable data visualization, sampling, and analytics for research teams to improve model performance.
• Collaborate with researchers, infrastructure, and compliance teams to meet technical and governance requirements.

Skills

Python

Go

Scala

Spark

Ray

Airflow

Prefect

Kafka

Redis

Postgres

ClickHouse

GitHub APIs

Job description

Data Platform Software Lead Engineer – AI/ML Systems

Location: Hybrid (4 days onsite – Downtown Toronto)
Type: Full-Time | Start-up

About the Role

Join our team as a Data Platform Software Lead Engineer and drive the architecture that fuels our AI and ML pipelines, focusing on large-scale, code-based text datasets. You'll design and build reliable systems for data ingestion, transformation, and delivery—enabling teams to train, iterate, and deploy models with confidence.

What You’ll Do

Architect and implement scalable data platforms for code/text dataset ingestion, processing, and delivery.

Build web-scale crawling and metadata extraction tools from open-source code repositories.

Develop reliable, distributed pipelines with frameworks like Spark, Kafka, and Airflow/Prefect.

Enable data visualization, sampling, and analytics for research teams to improve model performance.

Collaborate with researchers, infrastructure, and compliance teams to meet technical and governance requirements.

Must-Have Skills

8+ years in data-intensive software engineering.

Proficiency in Python, Go, or Scala; Spark or Ray; Airflow or Prefect; Kafka; Redis; Postgres or ClickHouse; GitHub APIs.

Understanding of how datasets power AI/ML workflows.

Proven experience in scalable data infrastructure and pipeline development.

Skills in web crawling, scraping, and large-scale ingestion.

Cloud-native experience (e.g., AWS, containerized compute, security).

Bonus Points;

Curating or preparing code-based datasets for LLMs or AI tooling.

Familiarity with code parsing, tokenization, or embedding.

Startup or fast-paced high-ownership environment experience.

Why Work With Us

Influence the technical path of an AI-first startup.

Work with cutting-edge cloud and ML/AI technologies.

Receive competitive pay with equity.

Collaborate with a diverse, high-caliber team.

Thrive in a dynamic, innovation-driven workplace culture.

Screening Questions

Describe a scalable data pipeline you've built for processing large code or text datasets.

What tools or frameworks did you use for distributed data processing and why?

Share an example of a web crawling or scraping system you’ve developed at scale.

What components of your past data pipelines have you deployed in AWS (e.g., storage, compute, security)?

Have you worked with code-based datasets or applied parsing/tokenization in model workflows?

Apply Now

Interested? Submit your resume and a brief response to the screening questions to: [e-mail]