About DeepNeuronic

DeepNeuronic is a fast-growing AI startup building real-time computer vision systems for transportation, retail, and industrial automation applications. Our platform turns live video into actionable analytics at scale, across edge and cloud deployments, helping organizations improve safety, efficiency, and operational decision-making. As we scale across diverse customers and environments, we continuously invest in robust pipelines and data foundations

that keep our products reliable and scalable.

Introduction

Computer vision products only work well when the underlying pipelines are efficient, repeatable, and able to handle large volumes of data, videos, events, metadata, and model outputs, without becoming slow, fragile, or expensive.

In this role, you’ll focus on improving how DeepNeuronic ingests, processes, stores, and serves high-throughput data

across our products. You’ll optimize pipelines end-to-end, strengthen data handling practices, and help ensure results are consistent, traceable, and operationally scalable.

The Role

As an ML Engineer

, you’ll design and optimize data and inference pipelines that power DeepNeuronic’s computer vision products. You’ll work closely with the Product Tech Lead

and ML engineering teams

to improve throughput, reliability, and maintainability across the full pipeline lifecycle, from data ingestion and processing to outputs consumed by customers and internal teams.

What You’ll Be Doing

• Optimize high-throughput pipelines for video-derived data (events, tracks, analytics outputs, metadata).
• Improve data ingestion workflows (batch + streaming), ensuring robustness to noisy inputs and operational constraints.
• Design efficient data storage and retrieval strategies (schemas, partitioning, indexing, retention policies).
• Build pipeline observability: metrics, tracing, data-quality checks, and failure recovery patterns.
• Improve reproducibility and traceability: dataset/version lineage, pipeline versioning, and consistent output formats.
• Work with Product and Engineering to define stable interfaces between CV modules, backend services, and dashboards/APIs.

What You Need to Have

• 2–5+ years of experience in ML/Data Engineering, backend engineering with a data focus, or pipeline engineering roles.
• Strong Python skills and practical experience building production data workflows.
• Solid understanding of data systems: batch/stream processing, storage formats, indexing/partitioning, and performance trade-offs.
• Experience working with large datasets (high volume, high cardinality metadata, or video-derived outputs).
• Comfortable collaborating cross-functionally and translating product requirements into pipeline design.

Nice to Have

• Exposure to computer vision production pipelines (RTSP ingest, decoding, inference outputs, tracking/event logic).
• Experience with message queues / streaming systems (e.g., Kafka, Pub/Sub, RabbitMQ) and workflow orchestration (Airflow, Prefect, Dagster).
• Familiarity with cloud data services (GCP/AWS/Azure) and hybrid deployments.
• Experience with time-series and analytics stores (Postgres at scale, ClickHouse, BigQuery, Elastic, etc.).
• Familiarity with Docker and operating data services in production environments.
• Understanding of MLOps concepts (versioning, reproducible environments, experiment tracking).

What We Offer

• A high-impact role building the data backbone behind real-world AI products.

• Direct ownership of pipeline scalability and reliability across multiple verticals.

• A flat and agile environment with fast iteration and meaningful responsibility.

• Flexible hybrid work model and autonomy with clear outcomes.

The Hiring Process

• 1st take-home general skills assignment;
• 2nd take-home specific skills assignment (to be delivered within 1 week);
• 1h general & specific skills and working principles interview;
• 1h mindset and cultural fit interview.

Note

: Please note that this position does not offer relocation assistance. Candidates must possess a valid EU visa and be based in Portugal.

Our job titles may span more than one career level. Actual pay is determined by skills, qualifications, experience, location, market demand, and other factors. Compensation details listed in this posting reflect the base salary and any potential variable, bonus, or sales incentives, and the Company’s estimation of the value of private company stock options, if applicable. The pay range is subject to change, the future value of company stock options is not guaranteed, and compensation may be modified in the future.