Senior Machine Learning Engineer
Remote-US
Technology /
Full-time /
Remote
About Tala
Tala is on a mission to unleash the economic power of the Global Majority – the 4 billion people overlooked by existing financial systems. With nearly half a billion dollars raised from equity and debt, we are serving millions of customers across three continents. Tala has been named by the Fortune Impact 20 list, CNBC’s Disruptor 50 five years in a row, CNBC’s World's Top Fintech Company, Forbes’ Fintech 50 list for eight years running, and Chief's The New Era of Leadership Award. We are expanding across product offerings, countries and crypto and are looking for people who have an entrepreneurial spirit and are passionate about our mission.
By creating a unique platform that enables lending and other financial services around the globe, people in emerging markets are able to start and expand small businesses, manage day-to-day needs, and pursue their financial goals with confidence. Currently, over nine million people across Kenya, the Philippines, Mexico, and India have used Tala products. Due to our global team, we have a remote-first approach, and also have offices in Santa Monica, CA (HQ); Nairobi, Kenya; Mexico City, Mexico; Manila, the Philippines; and Bangalore, India.
Most Talazens join us because they connect with our mission. If you are energized by the impact you can make at Tala, we’d love to hear from you!
The Role
The Senior Machine Learning Engineer is an individual contributor who will build, monitor, and maintain Tala’s core machine learning and causal inference services and tooling. You will own customer-facing real-time streaming feature extraction and model inference, model-related batch compute platforms and jobs, service level objective definition and measurement, root cause analysis, software and architecture design, enterprise technical maturity assessment, highly effective cross-functional collaboration, and mentorship.
What You'll Do
- Develop Data Scientist and Analyst-friendly self-service tooling and frameworks to explore new data sources, extract new features, and train, test, deploy, and monitor models.
- Optimize the model development and software development life cycles.
- Maximize quality of models, services, and tooling with unit testing, integration testing, dry run and blue-green deployment, infrastructure-as-code, automation, observability, and fault tolerance.
- Write and review design documents, perform code reviews, and weigh in on implementation choices from other technical teams.
- Collaborate with and support cross-functional teams (Product, Data Platform, Credit, and Business Development).
What You'll Need
- 5+ years of backend software experience in consumer scale applications, at least 4 of them with Python
- 2+ of those years in real-time streaming data (Kafka, Kinesis, Beam, Flink, Spark Streaming)
- Experience autonomously building machine learning or causal inference models to solve business problems.
- Proficiency with machine learning tools and tech (Jupyter, Pandas, Scikit-Learn, Xgboost, Tensorflow, Pytorch, Hugging Face).
- Strong database experience, both relational and non-relational (MySQL, PostgreSQL, Cassandra, HDFS, Snowflake, Druid).
- Strong hands-on experience in cloud computing (AWS, GCP, Azure, Kubernetes).
- Experience with batch processing platforms (Airflow, Metaflow).
- Experience with API development for mobile/web use (REST, GraphQL, gRPC, Protocol Buffers).
- Strong collaboration experience with Data Science, Analytics, other Engineering teams, and business stakeholders.
- Knowledge of scalable algorithms.
- Prefer advanced degree in computer science, math, or related field.
$150,000 - $200,000 a year
Our vision is to build a new financial ecosystem where everyone can participate on equal footing and access the tools they need to be financially healthy. We strongly believe that inclusion fosters innovation and we’re proud to have a diverse global team that represents a multitude of backgrounds, cultures, and experience. We hire talented people regardless of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.
