Senior ML Engineer (Routing)

*instinctools is a software development company that provides custom software solutions for businesses of all sizes. Our team works closely with clients to understand their specific needs and provide personalized solutions that meet their business requirements.

*instinctools is looking for a Senior ML Engineer (Routing) for one of our clients. 

Location: Europe, Poland, Kazakhstan 

Client: is a global mobility and urban services platform that allows users to book rides or other services and negotiate the fare directly with the service provider. It offers a variety of services including ride-hailing, intercity travel, delivery, and task assistance, operating in over 700 cities across 47 countries and is the second most downloaded mobility app in the world.

Project Description: We are looking for a senior machine learning engineer who will design and build the machine learning systems that power ETA accuracy and routing quality at scale. The work spans deep learning model development, large-scale geospatial data pipelines, and low-latency production serving. You will measure your impact through offline evaluation metrics and the results of online experiments including shadow tests and switchback designs which feed directly into ETA accuracy, cancellations, and business outcomes.

Duration of cooperation: 6+ months

Working time zone: СЕТ

Responsibilities and tasks:

• Design and build ML models that correct and refine the routing engine’s ETA estimates, from gradient-boosted trees through to neural and Transformer-based architectures as data scale grows
• Develop traffic-estimation models that turn large-scale GPS data into road-level speeds and historical-traffic profiles, and feed them into the routing engine to produce time-of-day-aware ETAs
• Work on map-matching that snaps noisy GPS data onto the road graph, and the spatial aggregations that make movement and speed data usable for modeling
• Improve ETA calculation, smoothing, and rerouting logic to close the gap between predicted and actual arrival times across changing conditions
• Translate routing and ETA goals into ML objectives with the right proxy metrics and non-functional requirements, including loss formulations where under- and over-prediction carry different costs
• Lead evaluation end-to-end, from offline accuracy and routing-quality metrics to the design of online experiments including shadow tests and interference-aware designs such as switchbacks and prove a change improves accuracy before it ships
• Partner with backend engineers to take models from prototype to low-latency production serving that meets tight latency and throughput targets
• Partner with product and operations to turn routing and traffic analysis into concrete features and requirements, and help extend ETA capabilities across verticals
• Own the ML lifecycle in production – serving, monitoring for data and concept drift, and building the retraining pipelines that hold quality as traffic patterns, cities, and the map shift.

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