Senior Machine Learning Engineer in Medical Imaging

Oncoustics; Toronto; On-site; CAD 130,000 - 170,000; Full time

Job summary

Oncoustics in Toronto is seeking a Director/Senior Machine Learning Engineer in Medical Imaging to lead advanced AI work for liver care using ultrasound data. You will architect and deploy deep learning systems and collaborate with signal processing, clinical data, and product teams to drive production-ready solutions.

You will oversee model development, ensure robust QA, and lead a cross-disciplinary team while aligning with FDA planning and regulatory considerations.

Qualifications

• Graduate degree in a related field and an undergraduate degree.
• 5+ years of industry AI/ML experience in healthcare.
• Proficiency in Python for DL/ML: Pandas, Sklearn, Keras, TensorFlow, PyTorch.
• Deep Learning design with attention, multi-instance learning, and GANs.

Responsibilities

• Lead development and deployment of deep learning models in TensorFlow and PyTorch.
• Build on a collaborative, multi-disciplinary, distributed team culture.
• Coach ML scientists and engineers on ML software practices.
• Define ML engineering processes and pipelines between R&D and SW.
• Plan deployment of models to Oncoustics future apps.
• Participate in FDA planning and clearance of algorithms.

Skills

Python

TensorFlow

PyTorch

Keras

Pandas

Sklearn

Deep Learning

Attention mechanisms

Multi-instance learning

GANs

Education

Graduate degree in a related field

Undergraduate degree

Job description

Director/ Senior Machine Learning Engineer in Medical Imaging

TORONTO: RESEARCH AND DEVELOPMENT - FULL TIME

Job Description

Oncoustics is revolutionizing the use of point of care ultrasound in liver care through advanced AI. We are supported by high-profile institutional investors and have deep partnerships in place with several major ultrasound and pharmaceutical players. We are looking to hire a senior ML Engineer to join our team with relevant academic experience and deep industry experience.

A successful candidate will be capable of in-depth development and understanding of machine learning architectures that go beyond the state of the art—advancing beyond conventional hyper-parameter tuning on existing models. They should be able to manage and lead both teams and projects, while effectively coordinating with signal processing, clinical data, and product teams. The ideal candidate will have strong expertise in processes, pipelines, operations, QA, and writing production-grade code. They should be able to build on previous work to drive innovation with a novel dataset and demonstrate a collaborative, team-oriented mindset.

Responsibility

• Lead development and deployment of deep learning and machine learning models in tensorflow and pytorch
• Build on existing collaborative culture in multi-disciplinary & distributed team
• Coach on and explain ML engineering (options, cost-benefit, etc) to ML scientists and team overall
• Help the ML and software teams understand the necessary parts of ML to properly develop supporting systems and processes
• Coach both ML scientists and ML/software engineers on appropriate best ML software practices
• Work w/ R&D team to improve experiment foci (IE prioritization and targeted results) and communications (presentations, updates, etc)
• Define and manage ML engineering processes and pipelines between R&D and SW engineering for final product outputs.
• Design models/learning strategies to optimize mining of unique ultrasound signal data with deep learning
• Adopt state of the art algorithms and models for that include multi-instance learning strategies, attention and GANs
• Plan for deployment of all models in Oncoustics future apps
• Remain aware of new innovations in the use of AI/ML in medical imaging and ultrasound
• Participate in FDA planning and clearance of algorithms

Requirements

• Graduate degree in a related field, and relevant undergraduate
• 5+ years at least of industry experience in the development and deployment of AI/ML models for health care
• Expertise in Python for Deep Learning and Machine Learning packages: Pandas, Sklearn, Keras, Tensorflow, Pytorch
• Deep Learning architecture design: Experience with attention mechanisms, multi-instance learning and/or GANs

Good To Have

• PhD/graduate work in machine learning/machine vision
• Expertise in medical imaging analysis or signal processing with AIExperience in the use of ultrasound
• Prior experience with FDA clearance of AI/ML algorithms