Machine Learning Engineer II (Model Training)

Bison Group is seeking a Machine Learning Engineer to train, fine-tune and evaluate models for AI analysis tools. You will own the model training lifecycle, from dataset preparation through evaluation and deployment, and adapt models to domain-specific language and tasks. Hands-on model training experience is required; experience limited to hosted APIs or prompt engineering does not meet this requirement.

What you will do:

• Train and fine-tune open-weight language models using supervised fine-tuning and parameter-efficient methods such as LoRA/QLoRA. Improve performance on domain terminology, task instructions and structured outputs.
• Build instruction and preference datasets from transcripts, surveys and reference materials.
• Clean, deduplicate and label data; generate and validate synthetic examples with subject matter.
• Train or fine-tune embedding and reranker models for retrieval-augmented generation (RAG).
• Measure retrieval quality for specialized terminology and acronyms.
• Adapt speech recognition, speaker diarization and voice activity detection models for noisy audio, varied accents and conversations with multiple speakers.
• Build offline evaluation suites and regression tests covering citation and attribution accuracy, hallucination rate and latency. Demonstrate improvements against established baselines before Quantize, export and serve trained models on on-premises GPUs and constrained hardware.
• Balance model quality against memory use, inference speed and throughput.
• Create reproducible training workflows with experiment tracking, versioned datasets, checkpoints and model cards. Package models and dependencies for deployment in environments without internet.
• Partner with product, UX, analysts and subject matter experts to define useful model behavior.
• Document experiments and decisions so teammates can retrain, evaluate and maintain models.