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Open nowPosted 1259 days ago

Machine Learning Engineer

quincus2 open roles

Where
Toronto
Work mode
Remote
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Your applicationOpen nowMachine Learning Engineerquincus · Toronto

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  2. 3.4%3 days
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  4. 14.3%14 days
  5. 33.7%30 days
This job: posted 1259 days ago

The posting

“Make every logistics journey your best one yet”

The Company.

Founded in 2014, Quincus is a B2B supply chain operating SaaS platform headquartered in Singapore. We solve today's global supply chain challenges with groundbreaking technology. Using AI and machine learning, we have digitized and optimized the logistics process while giving customers full transparency into their supply chain.

Quincus was founded by two visionary entrepreneurs who possess more than a decade of experience in tech. Chief Product Officer Katherina-Olivia Lacey is leading a tech revolution in this space while empowering women in the supply chain industry. Jonathan E. Savoir, Chief Executive Officer, appeared on Forbes' 30 Under 30 Asia List in 2020, and also serves on the boards of several startups.

Overview.

Quincus Research is building the next generation of intelligent systems for all Quincus products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by skilled software engineers and research scientists. Quincus Research teams collaborate closely with other teams across Quincus, maintaining the flexibility and versatility required to adapt new projects and focuses that meet the demands of the world's fast-paced business needs.

Job Overview.

We are looking for a highly motivated and experienced machine learning engineer to join our team and help us develop and deploy deep learning and reinforcement learning algorithms at scale. As a machine learning engineer, you will be responsible for designing and implementing scalable systems for serving models, optimizing inference performance, and managing production workflows.

Responsibilities:

- Design and implement scalable systems for serving deep learning and reinforcement learning models.

- Optimize inference performance of deep learning and reinforcement learning models using techniques such as quantization, pruning, and distillation.

- Utilize GPU computing to accelerate model training and inference.

- Develop and deploy production workflows for training and serving machine learning models.

- Collaborate with data scientists and software engineers to design and implement machine learning systems.

- Monitor and improve the performance of machine learning models in production.

- Stay up-to-date with the latest research and techniques in deep learning and reinforcement learning.

Qualifications:

- Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field.

- 3+ years of experience in software engineering or machine learning engineering.

- Strong programming skills in Python (C++ or Java a plus)

- Experience with deep learning frameworks such as TensorFlow or PyTorch.

- Experience with GPU programming using CUDA, OpenCL, or similar libraries.

- Experience with distributed systems and cloud computing platforms such as Kubernetes, Docker, GCP, and AWS.

Preferred Qualifications:

- Ph.D. in Computer Science, Electrical Engineering, or a related field.

- 5+ years of experience in software engineering or machine learning engineering.

- Experience with reinforcement learning algorithms and frameworks.

- Experience with production deployment of machine learning models and implementation of APIs for big data.

- Strong understanding of computer architecture and performance optimization.

- Strong communication and collaboration skills.

If you are passionate about developing and deploying machine learning algorithms at scale, and want to join a dynamic team working on cutting-edge technology, we encourage you to apply for this position.

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