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Principal ML Engineer builds next-gen foundation models from petabyte-scale telematics data to assess driver risk, detect crashes, and improve road safety using transformers and self-supervised learning.
Lead a team to design, build, and deploy production-grade ML systems and pipelines using Python, PyTorch, and cloud-native tools to improve United Airlines' operations and customer experiences.
Build and optimize ML infrastructure and tooling to improve training, inference, and GPU utilization for Reddit’s AI systems, using Python and systems languages like Go or Rust.
Build and improve an AI shopping assistant that surfaces relevant listings, compares options, and suggests fair prices for millions of users using modern ML and LLM techniques.
Builds and deploys advanced ML models (time-series forecasting, NLP) for insurance claims processing, translating business needs into scalable production systems using Python, PyTorch/Keras, and cloud tools like BigQuery/GCP.
Build and deploy realtime intent-drift detectors for AI coding agents running on developer machines, using small open models and local-first Python tooling.
Builds and maintains pipelines to ingest, process, and version large-scale unstructured data (video/sensor logs) for AI/ML model training, ensuring clean, reproducible datasets for defense applications.
Develops AI/ML solutions for warehouse design automation, working with LLMs, RAG pipelines, and integrating models via FastAPI. Core technologies include Python, PyTorch, TensorFlow, Docker, and cloud infrastructure on Yandex Cloud.
Design and deploy scalable AI/ML solutions for customers, advising on distributed training and production-scale deployments while bridging technical and business needs.
Design and deploy reinforcement learning algorithms for robotic control and motion planning in dynamic environments, integrating with hardware and simulation tools like Isaac Gym and PyTorch.
Principal Machine Learning Engineer at Doctolib in Paris designs and standardizes AI/ML systems across healthcare applications, focusing on LLMs, agentic solutions, and regulatory compliance.
Data Scientist role at S&P Global in Gurugram, India, analyzing financial and business data to derive insights.
Builds and deploys AI/ML models (focusing on speech recognition and legal insights) in production, optimizing performance, latency, and GPU usage while integrating LLMs and agentic workflows where applicable.
Build and deploy production-grade AI models using PyTorch and LLMs to power analytics from NielsenIQ’s global retail data, collaborating with engineers and business teams.
ML engineer builds and optimizes RAG pipelines, fine-tunes VLM for technical docs, and prepares on-prem LLM/VLM inference for an AI platform serving engineers.
Build and deploy ML systems and LLM-powered features for a B2B wholesale marketplace, owning models from design through production monitoring.
Build pricing, forecasting, and recommendation models for a B2B wholesale marketplace using Python, SQL, and econometric techniques.
Praktikant:in entwickelt KI-Algorithmen für Qualitätskontrolle in der Serienproduktion, nutzt Python, TensorFlow/PyTorch und arbeitet mit Produktionsteams zusammen.
Internship advancing machine-learning algorithms for quality assessment in series production using Python, PyTorch, and TensorFlow. You will build prototypes and support digitalization teams in manufacturing.
Lead a data science team at S&P Global to build predictive models and analytics solutions for financial data and market insights.
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