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Senior Machine Learning Engineer



Software Engineering
Remote, OR, USA · Remote
Posted on Wednesday, March 8, 2023

About Kiva:

Kiva (kiva.org) is an international nonprofit with a mission to expand financial access to help underserved communities thrive. We run a global marketplace platform to crowdfund microloans for financially excluded entrepreneurs, farmers, and students around the world. Our organization combines the culture and technological passion of an internet start-up with the compassion and empathy of a non-profit to create impact and opportunity at global scale. Since 2005, we have raised more than $1.85 billion in loan capital for 4.6 million borrowers in 80 countries. Our lenders fund over $13 million in loans every month. With offices in San Francisco, Portland, New York, Nairobi, and Bangkok, Kiva's team includes 100+ employees and 400+ volunteers worldwide. Our team is growing as we pursue exciting new opportunities to create a financially inclusive world.

Kiva’s Values:

Impact first - This is why we exist. This is the drumbeat we march to. Every day.

Extreme ownership - Own it; you, your relationships, your impact. Insist that others support you and hold you accountable.

Be curious and bold - Never stop learning. Question assumptions. Take Risks and dream big.

Inclusion. Equity. Diversity. - Without reservation and without caveat. In our hiring, in our workplace, and in our impact in the world.

Honor and integrity - Do the most right thing in the most right way. Cherish diversity and respect each other.

Love and kindness always - Say what you mean. Mean what you say. And don't say it mean. Clarity. Courage. Kindness.

About Machine Learning @Kiva:

The Machine Learning team is tasked with creating impact by using data to solve problems around financial inclusion. This translates to a host of interesting and challenging problems spanning machine learning, experimentation, services, and infrastructure. The machine team has built algorithms and services to personalize our lenders’ search and discovery experiences on kiva.org, optimize our marketplace from a supply and demand perspective, and is looking to further scale ML/data science at Kiva. Our current ecosystem of infrastructure, tools, and skillsets includes AWS, GCP, Kubernetes, Snowflake, DynamoDB, Google Cloud Composer (Airflow), Python, Tensorflow, OR-Tools, Kotlin, Kafka, and PHP. We are looking for smart and motivated individuals to join our growing team.

About the Role:

The machine learning team is looking for a Senior Machine Learning Engineer to help build data-driven products using machine learning/data science. This is an opportunity to be an influencer of and a significant contributor to the direction and growth of the team. As a full-stack machine learning engineer, you will:

  • Help solve a variety of problems using data to influence the experience of millions of lenders and borrowers around the world.
  • Ideate, prototype, and productionize machine learning models.
  • Build and scale production-ready ML and back-end infrastructure and services.
  • Influence and help execute our product strategy by collaborating with product managers, business stakeholders as well as other engineers.
  • Help foster a spirit of innovation and collaboration both within the team and across the organization.
  • Work to create impactful and sustainable solutions to complex problems by taking bold and measured risks.
  • Balance your technical excellence with a high E.Q., showing up with a sense of empathy, awareness, and responsibility.
  • Share the knowledge you bring and gain generously with your peers to perpetuate a culture of engineering excellence.

This role can be either remote or based out of our San Francisco / Portland offices and will report to the Director of Data Science based in San Francisco. At this time, we can only consider applicants with authorization to work in the United States on a permanent, full-time basis; unfortunately, we cannot provide visa sponsorship.

About You:

  • You have a BS in a quantitative discipline (computer science, engineering, physics, mathematics, or a related field) or comparable work experience.
  • You have 3+ years of experience in prototyping and productionizing data and machine learning-driven products.
  • You have experience in one or more of the following - recommender systems, predictive modeling, reinforcement learning (e.g., multi-armed bandits), personalized search, computational optimization, natural language processing, deep learning, causal inference especially as applied to e-commerce/marketplaces/email marketing.
  • You have experience with Docker, Kubernetes, and workflow orchestration tools (e.g. Airflow).
  • You have coding skills in Python and SQL (nice to have Kotlin or Java experience).
  • You have exposure to distributed systems and architectures (such as Kafka).
  • You have the ability to communicate findings and recommendations to technical and non-technical audiences.
  • You are willing to learn, take initiative, and wear multiple hats as required.

We understand that there is no “perfect” candidate and encourage you to apply even if you feel like you don’t match every single bullet point above.

What we offer:

  • An opportunity to improve real lives, solve hard problems, and change the world
  • Friendly, supportive, and adventurous environment with a team of engaged colleagues
  • A comprehensive, industry-leading benefits package
  • Opportunities to connect with and learn from colleagues and partners around the world
  • Salary Range $120K - $150K; a final offer will be dependent upon a candidate’s location, skills and experience.

A diverse and inclusive workplace where we learn from each other is an integral part of Kiva's culture. We actively welcome people of different backgrounds, experiences, abilities and perspectives. We are an equal opportunity employer and a great place to work. Join us and help us achieve our mission!

We will only accept applications directly from candidates. Kiva will not be responsible for any recruiting agency fees, absent a formal agreement.