Find work in Canada.

328,852 jobs across our Canadian network. How we count

Jobs in Canada

25+ jobs

Email me new jobs Save this search

Added 1 day ago
Software Analyst
Hitachi Rail
Toronto, ON
Full-time
The Software Analyst contributes to the design, development, integration, and testing of low- to medium-complexity operationally critical and safety-critical software systems within the…
Added 1 day ago
Software Analyst
Hitachi Rail
Toronto, ON
Full-time
The Software Analyst contributes to the design, development, integration, and testing of low- to medium-complexity operationally critical and safety-critical software systems within the…
Added 1 day ago
Software Analyst
Hitachi
Toronto, ON · (RSBU) Toronto
Full-time
The Software Analyst contributes to the design, development, integration, and testing of low- to medium-complexity operationally critical and safety-critical software systems within the…
Added 1 day ago
Software Analyst
Hitachi
Toronto, ON · (RSBU) Toronto
Full-time
The Software Analyst contributes to the design, development, integration, and testing of low- to medium-complexity operationally critical and safety-critical software systems within the…
Added 2 days ago
Forward Deployed Engineer (FDE) (Mid/Senior Level)
Urban Ridge Supplies
Toronto, ON
Full-time
You have 3+ years (6-10 years for Senior levels) of software engineering or technical delivery experience, with at least one production system you'd be proud to walk us through - ideally in…
Added 2 days ago
Forward Deployed Engineer (FDE) (Mid/Senior Level)
Urban Ridge Supplies
Toronto, ON
Full-time
You have 3+ years (6-10 years for Senior levels) of software engineering or technical delivery experience, with at least one production system you'd be proud to walk us through - ideally in…
Added 3 days ago
Software Testing Specialist
EPITEC
Ottawa, ON
Contract
We are seeking a highly experienced Senior Automation Developer to join a dynamic software verification and validation team supporting the development of advanced automotive products. In…
Added 5 days ago
Development Security Specialist (Industrial Oil & Gas Applications)
Pengcorp Ltd.
Calgary, AB
Full-time
We are seeking a Senior Application Security & DevSecOps Engineer to establish, operationalize, and continuously improve our Secure Software Development Lifecycle (SSDLC) across enterprise,…
Added 5 days ago
Senior Software & Hardware Engineering Specialist – Aerospace
Shiv Software Experts
Dorval, QC
Contract
Role: Senior Software & Hardware Engineering Specialist – Aerospace
Added 7 days ago
IT Architect, Architecture & Analytics – Enterprise Analytics
Saputo Inc.
Mississauga, ON
Full-time
Strengthen data governance and compliance by partnering closely with the Analytics Governance and DevOps Specialist to ensure security, lineage, and quality controls are embedded in…
Added 8 days ago
Software and Hardware Senior Engineering Specialist
SHIV SOFTWARE EXPERTS LLC
Montreal, QC · Dorval, QC
$100,000 to $130,000 CAD / year
Full-time
In your role, you will review and assess supplier’s airborne software and electronic hardware designs to ensure they meet technical requirements and regulatory requirements. In order to…
Added 9 days ago
Senior ASIC Verification Engineer
USA Tech Recruit
Ottawa, ON
$150,000 to $200,000 CAD / year
Full-time
A specialist engineering consultancy supporting advanced ASIC, FPGA, and embedded systems development is partnering with a global technology company developing high-performance silicon for…
Added 9 days ago
Senior ASIC RTL Design Engineer
USA Tech Recruit
Ottawa, ON
$150,000 to $200,000 CAD / year
Full-time
A specialist engineering consultancy supporting advanced ASIC, FPGA, and embedded systems development is partnering with a global technology company developing high-performance silicon for…
Added 10 days ago
Senior Business Development Manager– AI and Incubation Team
Aptiv
Ottawa, ON
Full-time
…Incubation team. We have a strong foundation of products within the embedded and cloud software space to build on and take to the next level of technology with AI at the EDGE. This is your…
Added 18 days ago
Senior Firmware Engineer
Natus Medical Incorporated
Oakville, ON
Full-time
Analyzes, designs, programs, debugs and modifies software. Troubleshoots code for firmware (IC embedded code) applications. Work often involves analog and digital hardware and software…
Added 1 month ago
Senior Systems Architect/Embedded Firmware Lead
Calian in Space & Technology
Ottawa, ON
Full-time
Using a variety of software development tools suites, the candidate will be expected to architect and develop new capabilities on embedded system platforms. The candidate will also need to…
Added 19 days ago
Senior FPGA Design Engineer
USA Tech Recruit
Ottawa, ON
$150,000 to $170,000 CAD / year
Full-time
A specialist engineering consultancy supporting ASIC, FPGA, and embedded software development is expanding its team and is looking for a Senior FPGA Design Engineer to join a long-standing…
Added 17 days ago
AppOps, Senior Specialist
Interac Corp.
Toronto, ON · Interac Corp. Head Office
Full-time
The vacant AppOps, Senior Specialist role will report to the Leader, Product Enablement.
Added 18 days ago
Application Security, Senior Specialist
Interac Corp.
Toronto, ON · Interac Corp. Head Office
Full-time
The Application Security, Senior Specialist will play a critical role in safeguarding our ecosystem by ensuring that our applications are designed, built, and deployed securely. You will…
Added 18 days ago
Senior Firmware Engineer
Natus
Oakville, ON
Analyzes, designs, programs, debugs and modifies software. Troubleshoots code for firmware (IC embedded code) applications. Work often involves analog and digital hardware and software…
Added 1 month ago
Forward Deployed Engineer (FDE) (Mid/Senior Level)
Salesforce
Toronto, ON · Canada - Toronto
Full-time
You have 3+ years (6-10 years for Senior levels) of software engineering or technical delivery experience, with at least one production system you'd be proud to walk us through - ideally in…
Added 24 days ago
Senior AI Platform & MLOps Specialist
Schneider Electric
Montreal, QC
Full-time
Embedded within the AI team, you will develop reusable, self-service platform capabilities for AI engineers and product development teams. You will work closely with the Principal Azure…
Added 1 month ago
Machine Learning Resident – Client: OpenCycle (12 month term)
Amii (Alberta Machine Intelligence Institute)
Edmonton, AB
Full-time
…decades of professional acoustics consulting: its founders and senior staff have spent their careers on noise impact assessments, complaint investigations, mitigation design, and regulatory…
Added 1 month ago
AI Solutions Engineer
Thales
Ottawa, ON
Full-time
This position requires direct or indirect access to hardware, software or technical information controlled under the Canadian Export Control List and/or the US the Export Administration…
Added 1 month ago
Electronics Engineering Research Specialist
Pegasus Imagery
Edmonton, AB
Full-time
Learn from senior engineers while growing into increasing technical ownership over time.

Amii (Alberta Machine Intelligence Institute)

Machine Learning Resident – Client: OpenCycle (12 month term)

Edmonton, AB, Canada

Job type: Full-time
Posting language: English
Posted: Aug 20, 2026

Apply About employer

Applications open in a new tab.

Original job description

“If you are interested in the application of machine learning to real-world audio for monitoring the noise industry puts into people’s lives, this is the right opportunity for you. Be a part of the team of research and machine learning scientists building acoustic intelligence for industrial sites from the ground up and get mentored by some of the best minds in AI during the process.”

Kunwar Saaim, Machine Learning Scientist, Advanced Technology

About The Role

This is a paid residency that will be undertaken over a 12-month period with the potential to be hired by our client, OpenCycle, afterwards (note: at the discretion of the client). The Resident will report to an Amii Scientist and regularly consult with the client team to share insights and engage in knowledge transfer activities. Successful candidates will be members of a cross-functional project team with backgrounds in ML research, project management, software engineering, and new product development. This is a rare opportunity to be mentored by world-class scientists and to develop something truly impactful.

About The Client

OpenCycle is a Calgary-based acoustic compliance and site-management platform for the energy, municipal, and heavy industries. The company grew out of four decades of professional acoustics consulting: its founders and senior staff have spent their careers on noise impact assessments, complaint investigations, mitigation design, and regulatory negotiation across Alberta, British Columbia, Saskatchewan, and Manitoba. OpenCycle exists to turn that hard-won expertise into software, so that noise compliance - which has traditionally meant months of specialist fieldwork and manual reporting - can be predicted, documented, and cleared in days.

Today the platform combines regulatory modelling, site and asset management, and a growing fleet of in-house-designed acoustic monitoring hardware deployed at customer sites across Alberta and BC. Machine learning is not a side project here - it is the core of the company’s next generation of product, developed by an in-house engineering team working directly alongside practising acousticians. That proximity is the point: models are specified, labelled, sanity-checked, and ultimately signed off by domain experts inside the same organization, and there is a short, direct line from a research result to a sensor running in a field.

The company’s mission is to reduce the impact industrial emissions have on people’s lives. Noise is where OpenCycle starts, because it is the emission that most directly affects the communities living next to energy and infrastructure development - and because it is the problem this team knows better than anyone.

About The Project

Environmental noise compliance today answers one question well: how loud was it? A sound level meter returns a number. What it cannot say is what made the noise. Attribution - deciding which of the several sources on and around a site is responsible for the level measured at a home or a receptor - is still done by an acoustician listening to recordings by hand. It is the most expensive and least scalable step in the entire compliance workflow, and it is the step this project automates.

The technical problem. The system has to answer four questions from a single learned representation: is a sound source present, what kind of source is it, which specific physical unit is it, and is that unit operating normally? It has to do this in the open air, where several sources overlap continuously and the interesting one is rarely the loudest. And it has to do it on a battery-powered outdoor node with microcontroller-class compute, reporting over a long-range radio link whose payload is measured in tens of bytes. Sending the audio to a large cloud model is not an option, so the central research question is how much of this capability survives compression to an edge budget.

What already exists. This is not a greenfield exercise. There is a deployed fleet of sound-level-meter nodes running a separately certifiable IEC 61672 measurement chain and validated against reference instruments in the field; a working detection-plus-embedding architecture with quantized, radio-sized payloads; an on-device inference path verified stage-by-stage against the research reference; and a frozen benchmark protocol with cross-site and cross-device evaluation tiers, built deliberately to expose the failure modes this team has already been burned by. There is also a substantial written record of experiments - including the ones that failed, which are often the more useful half.

The open problems. Four, and the Resident would help shape which ones to attack. First, channel invariance: our identity embeddings are currently key on the recording channel rather than the source, and six remediation strategies plus the data-scale hypothesis have been eliminated under controlled comparison, pointing at a label-ontology root cause. Second, tracking and temporal accumulation: fusing observations of the same source over time is the single largest measured improvement we have, turning a hard single-look problem into a tractable one - making the tracker first-class and validating it on real polyphonic field recordings is the dominant unknown before deployment. Third, condition detection from scarce labels: whether the operating state can be represented separately from identity when almost no per-unit multi-state data exists publicly. Fourth, compression: distilling the cross-site stability of a large pretrained audio backbone into something that fits the sensor’s compute and bandwidth budget.

What the year looks like. Real deployed hardware and real field data rather than a benchmark exercise; a measurement culture that treats negative results as results and freezes evaluation protocols so experiments months apart stay comparable; scope to publish; and a direct path from a research result to a device in a field that changes how a regulator makes a decision affecting a community. The Resident will work with OpenCycle’s engineering team and its acousticians, with field access to the sensor fleet and to the domain experts who produce the ground truth.

Required Skills / Expertise

Are you passionate about building great solutions? You’ll be presented with opportunities to both personally and professionally develop as you build your career. We’re looking for a talented and enthusiastic individual with a solid background in machine learning, specifically deep learning for audio and acoustic signal processing - sound event detection, audio representation learning, and evaluation that holds up under real-world domain shift.

Key Responsibilities

Design, implement, optimize, and evaluate models for far-field acoustic sensing tasks - sound event detection, open-set source identification, and operating-condition / anomaly detection - from a shared learned audio representation.
Prepare, curate, and preprocess high-quality audio datasets for training or fine-tuning, and validating models, including feature front-end design (log-Mel, PCEN, per-channel and frequency-wise normalization), audio augmentation, label-ontology definition, and work with weak, scarce, or noisy annotations.
Utilize state-of-the-art pretrained audio backbones (e.g., PANNs / CNN14, AST, BEATs, CLAP) and ML frameworks, tools, and open-source libraries to enhance model performance, accelerate workflows, and optimize data processing.
Undertake applied research on ML and audio representation-learning techniques to address the limitations in existing models - in particular channel and recording-device invariance, open-set metric learning, and representing operating state separably from source identity.
Optimize ML pipelines to ensure efficiency, scalability, and real-time streaming processing capabilities on compute- and bandwidth-constrained hardware.
Collaborate with the project team and stakeholders to develop MVP and client-focused solutions.
Engage in regular client meetings, contributing to presentations and reports on project progress, and work directly with the practising acousticians who define and sign off on ground truth.

Required Qualifications:

Completion of an MSc. or PhD in Computer Science or Electrical / Computer Engineering (or a related graduate degree program) with specialization in audio, speech, or acoustic machine learning, signal processing, or a closely related time-series domain.
Demonstrated hands-on experience applying deep learning to audio - for example, sound event detection, audio classification, audio embedding and verification, or anomalous sound detection.
Working knowledge of audio signal processing fundamentals: spectrograms and their time–frequency trade-offs.
Proficient in developing and training, fine-tuning and evaluating machine learning and deep neural network models in PyTorch and/or TensorFlow.
Proficient in Python programming language and related ML frameworks, libraries, and toolkits (e.g., PyTorch, torchaudio, librosa, HuggingFace, ONNX).
Solid understanding of classical statistics and its application in model validation.
Familiarity with Linux, Git version control, and writing clean code.
A positive attitude towards learning and understanding a new applied domain (environmental acoustics and industrial site operations).
Must be legally eligible to work in Canada.

Preferred Qualifications:

Familiarity with and hands-on experience with far-field or environmental audio data.
Experience with audio embedding and metric learning for open-set recognition, and with domain generalization across recording devices and deployment sites.
Publication record in peer-reviewed academic conferences or relevant journals in machine learning (e.g., DCASE, ICASSP, INTERSPEECH).
Good to have: experience deploying models to edge or embedded hardware - quantization, distillation, and designing to a fixed compute budget, with tooling such as ONNX or TFLite.
Experience/familiarity with software engineering best practices.
Experience with deploying machine learning models in production environments or strong software engineering (or MLE) skills is a plus.

Non-Technical Requirements:

Desire to take ownership of a problem and demonstrate leadership skills.
Interdisciplinary team player enthusiastic about working together to achieve excellence.
Capable of critical and independent thought.
Able to communicate technical concepts clearly and advise on the application of machine intelligence.
Intellectual curiosity and the desire to learn new things, techniques, and technologies.

Why You Should Apply

Besides Gaining Industry Experience, Additional Perks Include

Work under the mentorship of an Amii Scientist for the duration of the project.
Participate in professional development activities.
Gain access to the Amii community and events.
Get paid for your work (a fair and equitable rate of pay will be negotiated at the time of offer).
Build your professional network.
The opportunity for an ongoing machine learning role at the client’s organization at the end of the term (at the client’s discretion).

About Amii

One of Canada’s three main institutes for artificial intelligence (AI) and machine learning, our world-renowned researchers drive fundamental and applied research at the University of Alberta (and other academic institutions), training some of the world’s top scientific talent. Our cross-functional teams work collaboratively with Alberta-based businesses and organizations to build AI capacity and translate scientific advancement into industry adoption and economic impact.

How to Apply

If this sounds like the opportunity you've been waiting for, please don’t wait for the closing September 4, 2026 to apply - we’re excited to add a new member to the Amii team for this role, and the posting may come down sooner than the closing date if we find the right candidate before the posting closes! When sending your application, please send your resume and cover letter indicating why you think you'd be a fit for Amii. In your cover letter, please include one professional accomplishment you are most proud of and why.

Applicants must be legally eligible to work in Canada at the time of application.

Amii is an equal opportunity employer and values a diverse workforce. We encourage applications from all qualified individuals without regard to ethnicity, religion, gender identity, sexual orientation, age or disability. Accommodations for disability-related needs throughout the recruitment and selection process are available upon request. Any information provided by you for accommodations will be kept confidential and won’t be used in the selection process.

Apply About employer

More about this job

Responsibilities

Design and optimize deep learning models for far-field acoustic sensing and sound event detection on compute-constrained hardware. Collaborate with acousticians and engineers to curate audio datasets and translate research results into deployed edge devices.

Requirements

Requires a postgraduate degree (MSc or PhD) in Computer Science, Electrical Engineering, or a related field with a specialization in audio or acoustic machine learning. Candidates must be proficient in Python, PyTorch/TensorFlow, and have demonstrated experience applying deep learning to audio data.

Skills

Deep Learning
Audio Signal Processing
Sound Event Detection
PyTorch
TensorFlow
Python
Audio Representation Learning
Model Quantization
Model Distillation
Linux
Git
Metric Learning
Edge Computing
Acoustic Sensing
Feature Front-end Design

Benefits

Mentorship from an Amii Scientist
Professional development activities
Access to the Amii community and events
Potential for full-time hire by OpenCycle

Visa sponsorship

Not detected in the job text

Hiring contact

Kunwar Saaim

Keywords

Machine Learning
Deep Learning
Audio Processing
Acoustics
PyTorch
TensorFlow
Python
PANNs
AST
BEATs
CLAP
HuggingFace
ONNX
TFLite
Librosa
Torchaudio
Edge AI
Sound Event Detection
Anomaly Detection
Metric Learning
Signal Processing
Environmental Noise
Linux
Git
Acoustic Intelligence
Channel Invariance
Domain Generalization
DCASE
ICASSP
INTERSPEECH

Report this job