The Role
Design and deploy deep learning systems for search ranking, personalized recommendations, geospatial search, and pickup-point selection. Translate business goals into ML objectives, lead offline and online evaluation, conduct A/B tests, and partner with engineering on production model serving and latency optimization. Own the production ML lifecycle, including monitoring, drift detection, retraining pipelines, and quality management across international markets.
Summary Generated by Built In
Geo Search runs the search that tens of millions of riders use to say where they are going, across many countries where the commercial maps everyone else leans on are often wrong, incomplete, or simply missing. Getting search right in those conditions is one of the hardest and highest-leverage problems at inDrive. The same search and location capabilities also power other verticals such as courier, cargo, and intercity.
The team owns place search and autocomplete, geocoding, ranking and personalization, location detection and pickup selection, integrations with external providers such as Google and 2GIS, and its own search screens on Android and iOS. It is measured on order conversion rate, search conversion, successful search sessions, and mean reciprocal rank.
We are looking for a Senior Data Scientist engineer who will design and build the machine learning systems that power search ranking and personalized recommendations at scale. The work spans deep learning model development, the use of large language models, and production deployment. You will measure your impact through offline evaluation metrics and the results of online A/B tests, which feed directly into user engagement and business outcomes.
Key Responsibilities
- Design and build the deep learning systems behind search ranking, session-based recommendations, and multi-objective personalization, learning from rider behavior and serving users across markets
- Make ranking consume geographic context and own the recommendation and ranking of pickup points
- Translate business goals into ML objectives with non-functional requirements
- Lead evaluation end-to-end, from offline metrics to the design of online A/B tests, and prove a change improves engagement before it ships
- Partner with backend engineers to take models from prototype to production, including model serving and latency
- Partner with the product manager and operations to turn behavior analysis into concrete features and requirements
- Own the ML lifecycle in production, serving, monitoring for drift and building the retraining pipelines that hold quality as data shifts
Skills, Knowledge and Expertise
- An academic background in a quantitative field such as Computer Science, Mathematics, or a related discipline will be a plus
- 5 or more years of machine learning engineering experience, to confirm against the Senior II level internally, with at least three of them building and deploying deep learning models in production
- Direct experience with search, NLP, ranking, recommendation, or relevance systems.
- Expert-level proficiency in Python and its core data science libraries and SQL (e.g., PySpark, Pandas, NumPy, Scikit-learn, PyTorch)
- The ability to design an ML system from scratch in at least one area, including data analysis, annotation, and processing through to a model serving in production
- Experience turning a business goal into an ML problem with the right proxy metrics and non functional requirements, and designing or substantially contributing to the A/B tests and statistical evaluation that prove impact on user behavior
- Experience using MLOps tools and practices to manage the ML model lifecycle
- Experience deploying models to production on ML serving infrastructure and optimizing for latency, and awareness of concept drift and how to detect and manage it
- The ability to influence teammates and partner teams, and to communicate complex results clearly
- Experience fine tuning and deploying large language models (or small language models), for query understanding or relevance
- Subject matter depth in geocoding or autocomplete relevance specifically
- Experience in mapping, location, or geospatial products
- Experience building for developing markets, where the underlying map and address data is weak
- Experience with BigQuery or Databricks certifications
- Subject matter depth in search, geocoding, ranking, or recommendation systems
- Experience in mapping, location, or geospatial products
Conditions & Benefits
- Help us challenge injustice by creating fair choices for millions of people across 1100+ cities in 48 countries.
- Develop your professional skills with access to mentoring, career consulting, and learning programs.
- Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
- Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
- Work alongside people who take initiative, speak openly, and challenge themselves to grow.
- Improve your language skills through co-financed courses and internal speaking clubs.
Final benefits may vary depending on the location.
About
inDrive is a global tech company on a mission to challenge injustice through fair choices. We started in 2012 in the coldest city on Earth, when a group of friends created a way for people to agree on fair ride prices. That idea grew into one of the world’s top ride-hailing apps, now with 400M installs across 48 countries.Today, we offer more than rides: from freight and delivery to intercity travel and financial services, all designed to put people first. We are committed to having a positive impact on people’s lives both through our core business, which supports local communities via a fair pricing model; and through the work of our impact programs. Ready to ignite your inner drive?
Skills Required
- 5 or more years of machine learning engineering experience
- At least 3 years building and deploying deep learning models in production
- Direct experience with search, NLP, ranking, recommendation, or relevance systems
- Expert-level proficiency in Python, core data science libraries, and SQL
- Experience designing an ML system from scratch, including data analysis, annotation, processing, and production model serving
- Experience translating business goals into ML problems, defining proxy metrics and non-functional requirements, and evaluating impact through A/B testing
- Experience with MLOps tools and practices for managing the ML model lifecycle
- Experience deploying production models on ML serving infrastructure and optimizing latency
- Awareness of concept drift and methods to detect and manage it
- Ability to influence teammates and partner teams and communicate complex results clearly
- Academic background in Computer Science, Mathematics, or a related quantitative field
- Experience fine-tuning and deploying large or small language models
- Experience with geocoding or autocomplete relevance
- Experience with mapping, location, or geospatial products
- Experience building products for developing markets with weak map and address data
- BigQuery or Databricks certification
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
What We Do
inDrive is a global IT and transportation platform inDrive is one of the world’s fastest growing online ride-hailing services. Its services are available in over 749 cities in 46 countries throughout the world. The Company’s app has been downloaded over 150 million times. inDrive offers other services, including intercity transportation, freight and cargo services, as well as delivery services in different markets of operations. inDrive is based in Mountain View, California, and operates regional hubs in the Americas, Asia, the Middle East, Africa and the countries of the CIS, and employs over 2,000 people. In early 2021, inDrive achieved unicorn status after closing a $140m investment round with Insight Partners, General Catalyst, and Bond Capital, which valued the company at $1.23 billion.
