Machine Learning Engineer (Decision Systems)

About Us

Umaneo is a Canadian AI consulting firm composed of passionate, highly motivated experts on a mission to build custom AI solutions, with a fearless desire to disrupt the world for the best. The team takes a delightfully human approach to solve big business problems, delivered in elegantly designed solutions. What’s more, we attract some of the best talent and propose a human-centric, truly remote workplace, with an emphasis on developing our talented individuals to their true potential.

We work with clients who are global leaders in immersive entertainment, industrial automation, retail, and legal tech. Our crew has delivered over 30 AI projects spanning generative models, signal processing, time series, and computer vision. We work end-to-end, from project scoping and prototyping through deployment and monitoring, to deliver production-ready systems.

Position Overview

We are seeking a Machine Learning Engineer with strong experience building production systems, with a focus on signal-based machine learning, time-series data, and decision-driven systems.

In this role, you will design systems that transform continuous or structured inputs into meaningful outputs under operational and temporal constraints. These systems may operate in real-time or offline contexts, and often combine signal processing, machine learning, and rule-based logic.

You will contribute to the development of AI-enabled expert systems that integrate learned models with decision frameworks and heuristics, as well as generative workflows built on top of these systems. You will also contribute to a broader set of AI projects across our portfolio.

Key Responsibilities

Design and implement machine learning systems for:

* Signal-based and time-series data (e.g., audio, sensor data)

* Event detection, pattern recognition, and sequence modeling

* Systems that operate under latency, sequencing, or reliability constraints

Contribute to end-to-end ML workflows, including:

* Data acquisition and preprocessing

* Feature engineering (e.g., DSP, embeddings, statistical features)

* Model development, evaluation, and iteration

* Deployment and monitoring of production systems

Design systems under operational constraints, including:

* Latency, synchronization, and temporal consistency

* Robustness to noisy or imperfect inputs

* Trade-offs between accuracy, stability, and responsiveness

Develop hybrid decision systems, including:

* Systems combining machine learning models with rule-based decision logic and expert-driven heuristics

* Event-driven or state-based system behavior

* Integration of multiple signal sources into coherent decision pipelines

Participate in system design and architecture decisions, including:

* Modular and extensible system design

* Interfaces between data processing, decision layers, and downstream outputs

* Integration between real-time and offline or generative components

Work closely with stakeholders to:

* Translate business or creative goals into technical systems

* Iterate on system behavior based on real-world feedback

* Balance technical constraints with product requirements

Contribute to a variety of AI projects across domains, adapting to different problem settings, data modalities, and system constraints

Contribute to engineering quality and best practices:

* Write clean, maintainable, and well-documented code

* Participate in code reviews and technical discussions

* Ensure reproducibility and robustness of experiments

Qualifications

* Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or a related field

* Demonstrated ownership of production systems

* Strong programming skills in Python

Hands-on experience with at least one of the following:

* Signal processing (e.g., audio, sensor data, time-series feature extraction)

* Time-series modeling and event detection

* Systems involving sequential or temporal data

Experience with:

* Machine learning frameworks (PyTorch, TensorFlow, etc.)

* Designing and evaluating end-to-end ML pipelines

* Working with real-world, noisy, and continuous data

* Experience integrating machine learning into rule-based or decision-driven systems

Strong understanding of:

* Designing systems under constraints (latency, robustness, sequencing)

* Combining learned models with deterministic logic and heuristics

* Experience deploying systems in cloud or edge environments (Azure, GCP, AWS, or equivalent)

* Ability to adapt quickly to new domains and problem types

* Excellent communication skills and ability to collaborate in a cross-functional environment

What We Offer

* The opportunity to work on cutting-edge AI projects in entertainment, industrial automation, and beyond.

* Flexible working arrangements (remote-first team).

* A collaborative environment where your growth and contributions directly shape project outcomes.

* Exposure to a variety of real-world AI challenges.