Senior ML, Search & LLM Ops

🔥 0 minutes ago

🇪🇸 Spain – Remote

⏳ Contract/Temporary

🟠 Senior

⚙️ Operations

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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RavenPack

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

📣 Marketing

📦 Logistics

Consulting • Marketing • Logistics

RavenPack is a leading provider of technology and insights for data-driven organizations. Specializing in transforming unstructured data into actionable insights, RavenPack serves clients in the financial sector, including hedge funds, banks, and asset managers. The company offers a suite of solutions such as alpha generation, risk and compliance tools, and research products that enable quick extraction of valuable information from large datasets. RavenPack's offerings include news analytics, job analytics, regulatory filings, and sentiment indicators, catering to different levels of financial analysis, from company-level to macroeconomic indicators.

📋 Description

• Execute hands-on development, rapid prototyping, and plug-and-play optimizations for search and LLM systems • Implement and evaluate domain-specific open-source LLM adaptations using PEFT, including LoRA and QLoRA, and distillation for financial contexts • Prototype preference alignment mechanisms such as DPO and PPO • Benchmark and optimize model serving for low latency and high throughput • Apply AWQ and GPTQ quantization techniques and use Triton Inference Server, TEI, and vLLM • Prototype and validate advanced search techniques, including Matryoshka embeddings, late interaction models, and hybrid search pipelines • Combine structured and unstructured data in hybrid search pipelines • Package training, evaluation, and serving scripts into clean, reproducible deliverables using AWS SageMaker and Docker • Set up synthetic data generation and automated evaluation harnesses such as Opik and LLM-as-a-judge • Measure cost, latency, and quality trade-offs for delivered proofs of concept • Work as an independent contributor embedded with the internal Search & Recommendation engineering team

🎯 Requirements

• Master’s or PhD in Computer Science, Machine Learning, or a quantitative field (or equivalent practical experience) • Proven track record of delivering production-grade ML models and PoCs in Search, Information Retrieval (IR), or LLM infrastructure • Deep hands-on experience with Python, PyTorch, and the Hugging Face ecosystem (Transformers, PEFT, Accelerate) • Practical experience with model quantization, VRAM optimization, and high-performance serving frameworks (Triton, TEI, vLLM) • Experience using AWS SageMaker for training/deployment • Experience with experiment tracking and tracing tools (MLflow, Opik) • Ability to work autonomously and deliver well-documented, modular code • Ability to rapidly validate ideas through empirical testing • Fluent English communication skills, written and verbal • European legal working status required • EU timezone alignment required • Direct experience implementing RLHF or Direct Preference Optimization (DPO) is a bonus • Familiarity with financial market data and financial text domain processing is a bonus

🏖️ Benefits

• Potential contract extension based on project milestones and results • Competitive daily or project-based contract rate commensurate with experience • Fully remote working model • Equal opportunity workplace valuing diversity