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Software Engineer, Machine Learning InfrastructureStripe • Toronto, Canada
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Software Engineer, Machine Learning Infrastructure

Software Engineer, Machine Learning Infrastructure

Stripe • Toronto, Canada
Il y a 13 jours
Type de contrat
  • Temps plein
Description de poste
Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team

Stripe processes over $1T in payments volume per year, which is roughly 1% of the world’s GDP. The tremendous amount of data makes Stripe one of the best places to do machine learning. The ML Infra team builds services and tools that power every step in the ML lifecycle, including data exploration, feature generation, experimentation, training, deploying, serving ML models, and building LLM applications. With the phenomenal developments happening in the field of AI, we are positioned to accelerate the adoption of AI/ML across all parts of the company by building highly scalable and reliable foundational infrastructure. What you’ll do

You will work closely with machine learning engineers, data scientists, and product engineering teams to enable seamless end-to-end experience in building solutions across data, analytics, and AI/ML platforms. You will build the next generation of ML Infra services and major new capabilities that substantially improve ML development velocity and MLOps maturity across the company. Responsibilities

Designing and building scalable, reliable, and secure services for notebooks, ML model training, experimentation, serving, and LLM applications across multiple regions.

Creating services and libraries that enable ML engineers at Stripe to seamlessly transition from experimentation to production across Stripe’s systems.

Working directly with product teams and ML engineers to improve their day-to-day productivity.

Taking ownership of and finding solutions for technical and product challenges by working with a diverse set of systems, processes, and technologies.

Who you are

We’re looking for people with a strong background or interest in building successful products or systems; you’re passionate about solving business problems and making impact, you are comfortable in dealing with lots of moving pieces; and you’re comfortable learning new technologies and systems. You are comfortable working with other Stripe teams across the US and Canada. 2+ years of professional software development experience with a solid background on service oriented architecture and large-scale distributed systems

Experience working through the full life cycle of software development, from talking to users, to design and implementation, to testing and deployment, to operations

Experience working on production ML platforms, MLOps solutions, or building LLM applications

Experience running operations for high availability, low latency systems

Experience partnering with other teams to drive business outcomes

A sense of pragmatism: you know when to aim for the ideal solution and when to adjust course

Preferred qualifications

Experience building and shipping production AI agents

Familiarity with the LLMs and LLM Frameworks

Experience training and shipping machine learning models to production to solve critical business problems

This role is available either in an office or a remote location (35+ miles or 56+ km from a Stripe office). Office-assigned Stripes spend at least 50% of the time in a given month in their local office or with users. This hits a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility about how to do this in a way that makes sense for individuals and their teams. A remote location is defined as being 35 miles (56 kilometers) or more from one of our offices. While you would be welcome to come into the office for team/business meetings, on-sites, meet-ups, and events, our expectation is you would regularly work from home rather than a Stripe office. Stripe does not cover the cost of relocating to a remote location. We encourage you to apply for roles that match the location where you currently live or plan to live. The annual salary range for this role in the primary location is CA$135,200 - CA$202,800. This range may change if you are hired in another location. For sales roles, the range provided is the role’s On Target Earnings (“OTE”) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. This salary range may be inclusive of several career levels at Stripe and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and specific location. Applicants interested in this role and who are not located in the primary location may request the annual salary range for their location during the interview process. Specific benefits and details about what compensation is included in the salary range listed above will vary depending on the applicant’s location and can be discussed in more detail during the interview process. Benefits/additional compensation for this role may include: equity, company bonus or sales commissions/bonuses; retirement plans; health benefits; and wellness stipends. At Stripe, we're looking for people with passion, grit, and integrity. You're encouraged to apply even if your experience doesn't precisely match the job description. Your skills and passion will stand out—and set you apart—especially if your career has taken some extraordinary twists and turns. At Stripe, we welcome diverse perspectives and people who think rigorously and aren't afraid to challenge assumptions. Join us.

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Software Engineer, Machine Learning Infrastructure • Toronto, Canada

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