---
title: 'Machine Learning Engineer at Quincus'
canonical: 'https://feeny.ai/job/machine-learning-engineer-quincus-toronto-j1anzje7qnxt'
type: 'job'
last_seen: '2026-10-13'
---

# Machine Learning Engineer at Quincus

- **Company:** Quincus
- **Location:** Toronto, Canada
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2023-04-22
- **Last confirmed live:** 2026-10-13
- **Apply:** https://jobs.lever.co/quincus/51f562da-f4ed-45f9-a8e2-4c56b02bae13

## Job description

“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.

## About Quincus

## Company Overview
- **One-liner**: Quincus builds a decision engine that optimizes routing, pricing, and network design for physical supply chains – ports, air cargo, freight forwarding, and healthcare logistics.
- **Entity Type**: Private (Series B – most recent round led by AE Industrial Partners in November 2021)
- **Headquarters**: Singapore, Singapore
- **Founded**: 2014
- **Founders**: Jonathan Savoir (Co-Founder & CEO), Katherina Lacey (Co-Founder & CPO)

## Core Business
- **Primary industry/industries**: Supply chain logistics optimization, maritime and port operations, air cargo, freight forwarding, healthcare logistics, agricultural logistics, bulk energy.
- **Target customers**: Enterprise – sovereign port authorities, air cargo carriers, freight forwarders, national healthcare supply operators, terminal operators.
- **Mission or purpose statement**: Quincus strives to solve the most pressing supply chain problems by empowering the entire ecosystem – employees, vendors, customers, and hubs – to instantly respond to market changes using real-time visibility and machine learning optimization. The platform acts as a single source of truth that enables everyone to make decisions based on the same data. [builtin.com](https://builtin.com/company/quincus/)

## Products & Services
- **Quincus Optimization Engine**: A core engine covering six problem classes – routing and sequencing, pricing and yield, cost-to-serve, stochastic modeling, estimation and ground truth, and network design. It has run continuously against real operations for 12 years and is protected by a granted U.S. patent on stochastic maritime and supply chain optimization. [quincus.com](https://quincus.com/)
- **Sector Platforms** (layered on top of the same engine):
  - **Maritime and Ports**: Sovereign port authority optimization at national scale.
  - **Air Cargo**: Capacity, pricing, and offer management for carriers and handlers.
  - **Freight Forwarding**: Multimodal execution and route optimization.
  - **Healthcare Logistics**: National healthcare supply operations with tenant isolation.
  - **Agricultural and Perishable**: Supply resilience where the window is set by biology.
  - **Bulk and Energy**: Berth, jetty, and corridor intelligence for terminal operators.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed.
- **Key Metric**: Annual revenue estimated at **USD 14.2 million** (source: LinkedIn company data). Total funding amount not publicly disclosed; known rounds include:
  - Grant (Sep 2021) – USD 18,481, led by Singapore Tourism Accelerator
  - Seed Round (Jan 2015) – 1 investor
  - Series A (Mar 2020) – 3 investors, led by GGV Capital
  - Series A (Apr 2024) – 1 investor (unconfirmed)
  - Series B (Nov 2021) – 3 investors, led by AE Industrial Partners
- **Notable Investors/Partners**: GGV Capital, AE Industrial Partners, Singapore Tourism Accelerator.
- **Growth Signals**:
  - 7‑country operating footprint and 22 live production integrations.
  - Opened an R&D office in Toronto, Canada in 2022 with a “female-dominated” team.
  - Active in multiple verticals (port, air cargo, healthcare, agriculture).
  - LinkedIn follower base of 20,141 (steady year‑over‑year growth).

## Competitive Advantages
- **12 years of continuous production** on the same core engine – the system has never been a research prototype; it has run live freight, air cargo, port, and healthcare logistics operations.
- **Patented technology** – a granted U.S. patent on stochastic maritime and supply chain optimization.
- **Uncertainty‑first engineering** – the engine treats uncertainty (demand shifts, capacity decay, weather, chokepoints) as a primary object rather than an average to smooth away.
- **Network‑wide optimization** – pricing, routing, and network design are solved from the same underlying model, not separate silos.

## Strategic Focus
- **Expand sector platforms** – build sector‑specific offerings (healthcare, perishable, bulk energy) on top of the same core to address industry‑specific constraints (e.g., biological windows in agriculture).
- **Prove value against ground truth** – engagements start with a bounded problem and a measurable baseline; the platform expands only after demonstrating real‑world savings.
- **Geographic and vertical growth** – the Toronto R&D office signals further investment in machine learning and engineering talent outside Singapore.

## Why Work Here
- **Engineering culture** – the company hires people who “think in systems” and are comfortable being measured against ground truth. The team includes world‑class AI, supply chain, and modeling engineers.
- **Remote and hybrid options** – the Toronto office offers remote or in‑office roles; Singapore headquarters is hybrid. A remote work program is available.
- **Stock purchase plan** – employees are offered an employee stock purchase plan.
- **Innovation environment** – work on production optimization systems that directly impact national‑scale logistics (ports, healthcare supply chains).
- **Diversity** – the Toronto R&D office was announced as female‑dominated.

## Sources
1. [quincus.com](https://quincus.com/)
2. [quincus.com/company](https://quincus.com/company)
3. [builtin.com/company/quincus/](https://builtin.com/company/quincus/)
4. [linkedin.com/company/quincus](https://linkedin.com/company/quincus)
5. [jobs.lever.co/quincus](https://jobs.lever.co/quincus)

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