Senior ML Specialist
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Job Description
Senior ML Specialist building and evaluating agentic AI for Sciene, empowering Quartile’s retail media optimization platform. Owning model strategy, evaluation, fine-tuning, and production operations.
Responsibilities:
- Own model strategy across the platform by evaluating and selecting models per task
- Use benchmarks and LLM-as-judge harnesses with statistically sound analysis to support model decisions
- Design and maintain evaluation methodology, including offline eval sets, judge calibration, regression benchmarks, and quality metrics
- Build classification and fine-tuned models for routing, categorization, and detection tasks
- Construct datasets, train, evaluate, and deploy models
- Design, develop, and ship agentic AI products, including agent identities, reusable skills, tool integrations, and structured outputs
- Perform prompt and context engineering, including system prompt assembly, live-data context injection, thread/memory management, and Pydantic structured outputs
- Own output quality through deterministic enforcers, validation quality gates, and LLM-as-judge evaluators
- Diagnose hallucination, inconsistency, and model-version drift and implement fixes through prompts, retrieval, guardrails, or model selection
- Build and extend agent tools querying Databricks, MongoDB, and external systems
- Integrate agents with the broader ecosystem through the Model Context Protocol (MCP)
- Operate shipped services with OpenTelemetry and monitor quality, latency, and cost in Grafana
- Collaborate with data engineers, software engineers, and product teams on end-to-end AI solutions
- Raise team ML fundamentals through reviews and knowledge sharing
- Critically evaluate research literature and apply validated techniques
- Document and maintain the codebase while ensuring code quality and best practices
Requirements:
- 5+ years of applied machine learning experience, including training, evaluating, and deploying models
- Strong foundation in statistics and probability, including hypothesis testing, confidence intervals, sampling, sample-size reasoning, bias/variance, and calibration
- Deep understanding of LLM and generative AI theory, including transformers, attention, tokenization, embeddings, training pipelines, decoding strategies, scaling behavior, and failure modes
- Hands-on classical ML and NLP experience, including classification, clustering, feature engineering, text classification, embeddings, and semantic similarity
- Experience fine-tuning models using LoRA/PEFT, supervised fine-tuning, or full fine-tuning; ability to build training datasets
- Experience designing AI evaluation methodology, including offline evaluation sets, LLM-as-judge, inter-rater agreement, regression benchmarks, and significance testing
- Production-grade Python experience with PyTorch, scikit-learn, Hugging Face, modern async Python, FastAPI, Pydantic, tests, and CI
- Hands-on experience with at least one major LLM provider API: OpenAI, Anthropic, or Google; structured outputs and tool/function calling
- Master's or PhD in Machine Learning, Statistics, Computer Science, Mathematics, or related quantitative field, or equivalent demonstrated depth through publications, competition results, released models, or research code
- Senior-level autonomy owning problems end to end
- Preferred: NLP/ML publications or open-source contributions; Kaggle or equivalent competition record
- Preferred: retrieval systems, agent evaluation, model cost/latency optimization, Databricks, Azure, Docker, observability stacks, MCP, and 0-to-1 product delivery experience
Benefits:
- Equal opportunity employer committed to diversity, inclusion, and belonging
- PJ contract arrangement
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