You will join a product team in QuantumBlack Labs. You will work with data scientists, data engineers software engineers, other machine learning engineers, product managers, and designers to create innovative products and new technologies that facilitate, accelerate and enable the development and deployment of Artificial Intelligence / Machine Learning solutions at scale in production.

Operating like an “internal start-up”, we’ve already done a lot to be proud of, such as: 

  • Designing products that can explain complex data landscapes and insights to our users
  • Building frameworks and libraries for data scientists and data engineers to work in large-scale, complex projects. We open-sourced some of these frameworks, such as our award-winning Kedro or CasualNex
  • Codifying the methodology by which we deliver advanced analytics projects to our clients and the tooling needed to support them

Our engineers particularly love about QuantumBlack Labs:

  • Autonomy– Your users sit on the desk next to you, giving you unparalleled insight into key problems and the ability to design solutions iteratively, with literally rapid feedback. The fact that our developers have such good access to users makes them great candidates to feed into the product lifecycle and suggest the next big thing!
  • Variety & Ownership Mindset– You’ll be part of an ecosystem of very different products, providing unique learning and development opportunities. You’ll not be just an engineer but a core part of the product team driving product decisions using your engineering creativity, where you’ll interact with users, work with designers and product managers, give presentations and talks. Our team frequently engages in efforts they are passionate about outside their core product team, such as our Analytics for Social Good initiative, sharing their experience during Lightning Talks, joining our Toastmasters group, and many others.
  • A Collaborative, Multi-disciplinary Environment– Our teams include machine learning engineers, creative technologists, product managers, and designers with various experience spikes who work collaboratively and are passionate about their work.


You’ll design, develop and deploy real-world machine learning systems that facilitate, accelerate and enable advanced AI solutions at scale. Part of a core product team, you’ll drive decision making using your engineering creativity, and thrive in an “internal start up” culture. You will build state of the art analytics libraries, products, services and tooling that solve real-world client problems.


As a Software Engineer, Machine Learning, you will:

  • Build real-world scalable machine learning pipelines and deploy them to production
  • Operate at the intersection of data science and software engineering to create analytics solutions
  • Produce high-quality code that allows us to put solutions into production
  • Choose and use the right analytical libraries, programming languages, and frameworks for each tas
  • Build analytics libraries and tooling based on project experience and latest research, refactor code into reusable libraries, APIs, and tools
  • Play an active role in leading team meetings and workshops to inform product development and process evolution

Our tech stack:

While we advocate for using the right tech for the right task, we often leverage the following technologies: Python, PySpark, the Python Scientific Stack; MLFlow, Grafana, Prometheus for machine learning pipeline management and monitoring; SQL, Airflow, Databricks, our own open-source data pipelining framework called Kedro, Dask/RAPIDS; Django, GraphQL and ReactJS for horizontal product development; container technologies such as Docker and Kubernetes, CircleCI/Jenkins for CI/CD,  cloud solutions such as AWS, GCP, and Azure as well as Terraform and Cloudformation for deployment, and many more!

However, we advocate using the right tech for the right task. Technology evolves and engineering is responsible to stay up to date with the latest technologies and ensure we make the relevant changes where needed.

What you'll benefit from:

  • Fusing Tech & Leadership – We work with the latest technologies and methodologies and offer first-class learning programs at all levels.
  • Multidisciplinary Teamwork - Our teams include data scientists, engineers, project managers, UX and visual designers who work collaboratively to enhance performance.
  • Innovative Work Culture – Creativity, insight, and passion come from being balanced. We cultivate a modern work environment through an emphasis on wellness, insightful talks, and training sessions.
  • Striving for Diversity – We recognize the benefits of working with people from all walks of life.
  • Continuous development and progression – We offer an extensive choice of training sessions, ranging from workshops to international conferences, tailored to your needs as well as a personal mentorship system. We have multiple career paths and geographic locations to evolve within the Firm.
  • Global community – you'll learn from colleagues around the world by connecting both internally and externally through our various hosted meet-ups.

Visit our Careers site  to watch our video and read about our interview processes and benefits.

Proven experience designing and implementing Machine Learning systems (2+ years of relevant professional experience preferred)

Familiarity with distributed computing frameworks (Spark, Dask), cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), automation (CircleCI/Jenkins) and analytics libraries (pandas, numpy, matplotlib), infrastructure as code (Terraform)

Ability to write clean, maintainable, scalable and robust code in an object-oriented language (Python, Java) in a professional setting

Familiarity with the foundations of statistics and machine learning techniques

Practical knowledge of software engineering concepts and best practices, inc. testing frameworks and libraries, automation

Practical experience with commercial applications of libraries, frameworks, tools for manipulating large volumes of data

Master's degree in computer science, engineering or mathematics, or equivalent experience

Is a Remote Job?
Hybrid (Remote with required office time)
Employment Type
Full time

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