ज्या
पार्टनर फिडं
ML Research Engineer
इनापकथं मू
विवरण
- लजगाःया ताजि
- पूर्णकालीन
- रिमोट
- मखु
- कम्पनि ४.
- Npv
विवरण ३.
We're looking for ML Engineers to join White Circle , an AI Safety company building the safety, reliability, and optimization layer for AI systems through natural-language policies it automatically tests, enforces, and improves at scale. Backed by $70M (Series A) from top funds and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, and others, White Circle processes 100M+ API calls monthly and fine-tunes and trains its own LLMs to run faster and cheaper than open or proprietary models.
You will
• Turn petabytes of unstructured text into a structured, explorable view: topics, clusters, segments, trends, anomalies.
• Build scalable representation pipelines: sampling, preprocessing, embeddings at scale, indexing, and retrieval.
• Use LLMs for labeling, weak supervision, data enrichment, and automated diagnostics, with cost/quality controls.
• Translate findings into product and operational decisions, and ship self-serve datasets, data models, and dashboards.
• Work with engineering and research to align pipelines with production constraints (latency, cost, privacy).
Requirements
• Strong Python and SQL, with production-grade pipeline engineering (not just notebooks).
• Applied NLP/ML on real-world text: embeddings, clustering, topic modeling, semantic search, classification.
• Experience at scale: distributed processing, large-scale storage and querying, performance-cost tradeoffs.
• Evaluation of fuzzy problems: offline/online metrics, human-in-the-loop labeling, inter-annotator agreement, drift monitoring.
• Prior work with safety/moderation datasets, policy/rule systems, or high-volume logging/observability.
• Relocation to Paris or London (hybrid) required.
Bonus
• Public builder footprint: open-source models, datasets, or frameworks on HuggingFace/GitHub, papers, or technical posts.
• Experience at a frontier or near-frontier lab, or leading open-source model releases.
• RL for LLMs beyond standard RLHF: online RL, GRPO-style methods.
• Moderation, safety, or classification models at scale; multilingual model training.
We offer
• Competitive salary + equity.
• Hybrid work from central London or Paris office, relocation support for Paris after probation.
• Premium private health insurance, mental health support, flexible time off.
• Lunch and dinner covered in the office, L&D budget, all hardware and tools you need.
• Team off-sites twice a year.
Find Jobs in France on Arbeitnow
Source: Arbeitnow (https://www.arbeitnow.fr/jobs/companies/npv/ml-research-engineer-paris-357030)
You will
• Turn petabytes of unstructured text into a structured, explorable view: topics, clusters, segments, trends, anomalies.
• Build scalable representation pipelines: sampling, preprocessing, embeddings at scale, indexing, and retrieval.
• Use LLMs for labeling, weak supervision, data enrichment, and automated diagnostics, with cost/quality controls.
• Translate findings into product and operational decisions, and ship self-serve datasets, data models, and dashboards.
• Work with engineering and research to align pipelines with production constraints (latency, cost, privacy).
Requirements
• Strong Python and SQL, with production-grade pipeline engineering (not just notebooks).
• Applied NLP/ML on real-world text: embeddings, clustering, topic modeling, semantic search, classification.
• Experience at scale: distributed processing, large-scale storage and querying, performance-cost tradeoffs.
• Evaluation of fuzzy problems: offline/online metrics, human-in-the-loop labeling, inter-annotator agreement, drift monitoring.
• Prior work with safety/moderation datasets, policy/rule systems, or high-volume logging/observability.
• Relocation to Paris or London (hybrid) required.
Bonus
• Public builder footprint: open-source models, datasets, or frameworks on HuggingFace/GitHub, papers, or technical posts.
• Experience at a frontier or near-frontier lab, or leading open-source model releases.
• RL for LLMs beyond standard RLHF: online RL, GRPO-style methods.
• Moderation, safety, or classification models at scale; multilingual model training.
We offer
• Competitive salary + equity.
• Hybrid work from central London or Paris office, relocation support for Paris after probation.
• Premium private health insurance, mental health support, flexible time off.
• Lunch and dinner covered in the office, L&D budget, all hardware and tools you need.
• Team off-sites twice a year.
Find Jobs in France on Arbeitnow
Source: Arbeitnow (https://www.arbeitnow.fr/jobs/companies/npv/ml-research-engineer-paris-357030)
थ्व धलः पार्टनर फिडं वःगु खः । स्रोत वेबसाइटय् आवेदन बियादिसँ ।
श्रोत: Npv
थाय्
पेरिस, फ्रान्स ३.
Npv या लिस्टिङ।
