Senior Data Scientist, LLMs

November 19

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Aderant

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SaaS • B2B • Artificial Intelligence

Aderant is a global provider of legal business management and practice management software that helps law firms run and modernize their back-office operations. The company offers cloud-based SaaS platforms and applications — including practice management, time & billing (iTimekeep), billing delivery (BillBlast), compliance (Onyx/OCG), recruiting, calendaring/docketing, analytics (Stridyn), and AI-driven work-to-cash automation — designed for large professional services and law firms. Aderant emphasizes AI, cloud transformation, security (SOC 2), and integrations to improve billing accuracy, compliance, financial operations, and operational efficiency for law firms worldwide.

501 - 1000 employees

Founded 1978

☁️ SaaS

🤝 B2B

🤖 Artificial Intelligence

📋 Description

• Own LLM product experiments end-to-end: problem framing, prompt design, data generation, model selection/fine-tuning, offline/online evaluation, and iteration. • Work with open-source models (e.g., Llama, Mistral/Mixtral, Qwen, T5/Flan) using Hugging Face/Transformers, PEFT/LoRA/QLoRA, TRL, Accelerate/DeepSpeed. • Build efficient inference stacks: quantization (8-bit/4-bit), batching, KV-cache, speculative decoding, vLLM/TGI/TensorRT-LLM, and model/endpoint autoscaling. • Design robust evaluation: task accuracy and generation quality (exact match, ROUGE/BLEU/BERTScore), safety/toxicity, hallucination rate, latency/throughput, cost per request, and win-rate from human review; set up an eval harness and dashboards. • Generate and curate training data: synthetic data, augmentation, preference data (for DPO/RLHF), labeling guidelines, data quality checks, and dataset versioning. • Implement RAG pipelines where useful: embeddings, retrieval, and context construction with vector stores (FAISS/Milvus/pgvector/Qdrant). • Ship production services: expose models via FastAPI, containerize with Docker, write tests, add logging/metrics/tracing, and collaborate on CI/CD. • Integrate third-party LLM APIs (OpenAI/Azure OpenAI, Anthropic, Google, Cohere) alongside open-source models; choose the right tool for quality, speed, and cost. • Champion safety & compliance: prompt/response guardrails, PII handling, rate limiting, and abuse monitoring. • Document and share findings, best practices, and reusable components.

🎯 Requirements

• Strong Python and PyTorch skills; comfortable with data tooling (pandas, NumPy) and experiment tracking (MLflow/W&B). • Proven experience building with LLM APIs and open-source LLMs, including prompt engineering and LLM evaluation. • Hands-on fine-tuning (LoRA/QLoRA or full/adapter-based) and data generation for supervised or preference-based training. • Production experience deploying model services with FastAPI and Docker; familiarity with monitoring and alerting. • Solid understanding of experimental design and statistics; experience with A/B testing and human-in-the-loop evaluation. • Clear communication and a product mindset; you iterate quickly and measure impact. • Nice to have Kubernetes, Ray, or distributed training/inference. • Vector databases and embedding workflows; LangChain or LlamaIndex. • Cloud experience (AWS/GCP/Azure) and GPU provisioning/optimization. • Observability (Prometheus/Grafana), OpenTelemetry, and structured logging. • Security/governance for AI systems (guardrails, secrets management). • Knowledge of speech/vision LLM add-ons (Whisper, CLIP) when relevant.

🏖️ Benefits

• Professional development opportunities