Machine Learning Engineer (Python)

About the role

Responsibilities

• Own ranking, relevance and recommendation for geo search end to end, from training data through to models serving in production
• Build the label pipeline that turns search sessions and completed rides into trustworthy training data, including correction for position bias and other presentation effects
• Train and own the models that order results, and the confidence model that decides per query whether we serve our own answer or fall back to an external provider
• Build query understanding for our markets, including cross-script matching, using large language models to label offline and distilling into models fast enough for the keystroke path
• Own evaluation end to end: the offline replay harness that scores recorded sessions against real rides, the error analysis that turns failures into work for the right team, and the online experiments that prove impact
• Keep models healthy after launch as data and cities shift
  
  

Qualifications

• 5 or more years building models that shipped and improved a production metric
• Direct experience with ranking, search relevance, or recommendation systems, including how they are evaluated
• Expert Python and SQL, fluency with gradient boosting, and working knowledge of transformers
• Experience taking a model to production and owning it afterwards
• The ability to work across teams, since search quality work generates data and engineering tasks that others own
  
  Preferred qualifications
  
  
• Depth in geocoding, autocomplete, or place search
• Learning to rank in practice: pairwise and listwise objectives, MRR, NDCG, Hit@k, and their failure modes
• Relevance tuning on OpenSearch or Elasticsearch, including analyzers
• Multilingual or cross-script search, for example Arabic and Arabizi or Urdu and Roman Urdu
• Distilling language models under a latency budget, and experience in mapping or geospatial products in developing markets

Benefits

• Help us challenge injustice by creating fair choices for millions of people across 47 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.

Обязанности:
-Разработка и внедрение моделей машинного обучения и глубокого обучения (ML/DL) для бизнес-задач.
-Оптимизация и поддержка существующих алгоритмов.
-Работа с большими данными: очистка, подготовка, анализ.
-Интеграция моделей в продакшн (API, микросервисы).
-Исследование новых подходов в области AI (LLM, генеративные модели).
Требования:
-Опыт работы от 2 лет в области ML/AI.
-Знание Python и библиотек: TensorFlow, PyTorch, scikit-learn.
-Опыт работы с SQL и NoSQL базами данных.
-Понимание архитектуры нейронных сетей, NLP, CV.
-Английский язык — уровень B2 и выше.
Будет плюсом:
-Опыт работы с облачными платформами (AWS, GCP, Azure).
-Знание MLOps и CI/CD.
-Участие в AI-хакатонах или open-source проектах.

Andersen is hiring an ML Engineer (Routing) for a project developing machine learning solutions for real-time routing optimization, ETA prediction, and large-scale geospatial data processing.

The customer is a global investment management firm providing tailored investment solutions to institutional and private clients. It combines financial expertise with long-term investment strategies to help clients achieve their objectives while adapting to changing market conditions. The organization focuses on innovation, responsible investing, and operational excellence, continuously enhancing its capabilities to deliver sustainable value and support long-term growth.

The project is focused on developing machine learning solutions to improve ETA accuracy and routing quality at a scale. It includes building production-grade ML models, processing large-scale geospatial data, and deploying low-latency inference systems for real-time routing optimization.

• Designing and building 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.
• Developing 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.
• Working 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.
• Improving ETA calculation, smoothing, and rerouting logic to close the gap between predicted and actual arrival times across changing conditions.
• Translating 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.
• Leading 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.
• Partnering with backend engineers to take models from prototype to low-latency production serving that meets tight latency and throughput targets.
• Partnering with product and operations to turn routing and traffic analysis into concrete features and requirements and help extend ETA capabilities across verticals.
• Owning the ML lifecycle in production – serving, monitoring data and concept drift, and building the retraining pipelines that hold quality as traffic patterns, cities, and the map shift.

• Experience in machine learning engineering for 5+ years, including at least 3+ years building and deploying deep learning models in production.
• Direct experience with regression, forecasting or prediction models on tabular, time series or event data (e.g. delivery time, demand, pricing, logistics), including gradient boosting (XGBoost, LightGBM, CatBoost).
• Expert-level proficiency in Python and SQL, along with core data science libraries (PySpark, Pandas, NumPy, Scikit-learn, PyTorch).
• Ability to design an ML system from scratch, covering data analysis, data processing and feature engineering from raw logs through to 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 A/B tests, switchback or shadow experiments and statistical evaluation that prove business impact.
• Experience using MLOps tools and practices to manage the full ML model lifecycle, including monitoring and automated retraining.
• Experience deploying models on ML serving infrastructure with a focus on low latency, and practical knowledge of how to detect and manage concept drift.
• Level of English – from Upper-Intermediate and above.

• Subject matter depth in ETA / travel-time prediction, traffic estimation, or routing-engine quality.
• Hands-on experience with open-source routing engines (e.g., Valhalla, OSRM, GraphHopper) and concepts such as map-matching, speed profiles, road-graph tiles, and historical traffic.
• Experience in mapping, location, or geospatial products.
• Experience building for developing markets, where the underlying map and address data is weak.
• Experience with cloud data and ML platforms such as BigQuery or Databricks (certifications is a plus), and with distributed deep-learning training.

• Experience in teamwork with leaders in FinTech, Healthcare, Retail, Telecom, and others. Andersen cooperates with such businesses as Samsung, Siemens, Johnson & Johnson, BNP Paribas, Ryanair, Mercedes, TUI, Verivox, Allianz, T-Systems, etc..
• The opportunity to change the project and/or develop expertise in an interesting business domain.
• Job conditions – you can work both fully remotely and from the office or can choose a hybrid variant.
• Guarantee of professional, financial, and career growth! The company has introduced systems of mentoring and adaptation for each new employee.
• The opportunity to earn up to an additional 1,000 EUR per month, depending on the level of expertise, which will be included in the annual bonus, by participating in the company's activities.
• Access to the corporate training portal, where the entire knowledge base of the company is collected and which is constantly updated.
• Bright corporate life (parties / pizza days / PlayStation / fruits / coffee / snacks / movies).
• Certification compensation (AWS, PMP, etc).
• Referral program.
• Private health insurance and sports compensation, depending on the type of employment.

Join us!