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Senior Software Engineer MLOps

Ripple ON Toronto, Ontario, Canada

Live

$401k · Senior Full-time Posted over 3 years ago 3 min read

Ripple’s mission is to enable payments every way, everywhere for everyone. We believe connecting traditional financial entities like banks, payment providers and corporations with emerging blockchain technologies and users is the path to an open, decentralized, and more inclusive financial future. This Internet of Value gives any internet-enabled person, application or device access to financial services that are transparent, fast, reliable, and cheap. Delivering this vision is a challenge of massive scale spanning $155 trillion in annual cross border fiat payments and the $1.5 trillion market of digital assets that has grown 10X in the last year.

We are looking for a Senior Software Engineer, Machine Learning Operations to join a new team charged with determining and delivering optimal liquidity for every customer in the world in a cost-effective, robust and scalable manner. Candidates will partner closely with applied scientists and engineers to build out a ML infrastructure to enable delivering scalable production machine learning services to solve these liquidity challenges. You will be responsible for the design and implementation of the infrastructure and tools to create and deploy production models. Ideal candidates will have a track record of technical excellence in designing, building and delivering reliable solutions as part of a team. As a member of a new initiative, you must be passionate about inventing and delivering customer-focused solutions to ambitious and ambiguous challenges.

WHAT YOU'LL DO:

Be an entrepreneurial builder, working up and down the data stack, mixing data engineering, machine learning and software engineering to jumpstart this new initiative.

Design and build effective, user-friendly infrastructure to enable scalable, auditable, compliant and maintainable machine learning services at Ripple.

Collaborate with machine learning engineers, software engineers and product managers to develop platform to support ML workflows including experimentation, training and production operations

Build and maintain systems with an infrastructure-as-code approach

Establish and manage the cloud stack (AWS or GCP or Databricks) required for ML resources

Design and implement tools and processes for the entire ML Ops including model development, model deployment, model serving, model monitoring and experimentation.

WHAT WE'RE LOOKING FOR:

Degree in computer science or other quantitative field; masters or PhD preferred

5+ years software engineering experience with 3+ years developing production machine learning systems preferred

Experience with general cloud Infrastructure and concept of infrastructure as code

Experience with machine learning lifecycle platforms (Kubeflow, MLflow) and cloud data services (GCP, AWS)

Experience with machine learning frameworks tools like scikit-learn, TensorFlow, or PyTorch

A collaborative coder, comfortable with Git and code reviews

Excellent written and verbal communication skills

Attention to detail and a commitment to excellence

WHAT WE OFFER:

The chance to work in a fast-paced start-up environment with experienced industry leaders

A learning environment where you can dive deep into the latest technologies and make an impact

Competitive salary and equity

100% paid medical and dental and 95% paid vision insurance for employees starting on your first day

401k (with match), commuter benefits

Industry-leading parental leave policies

Generous wellness reimbursement and weekly onsite programs

Flexible vacation policy - work with your manager to take time off when you need it

Employee giving match

Modern office in San Francisco’s Financial District

Fully-stocked kitchen with organic snacks, beverages, and coffee drinks

Weekly company meeting - ask me anything style discussion with our Leadership Team

Team outings to sports games, happy hours, game nights and more!

Ready to apply? You'll go straight to the hiring team.

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