Machine Learning Engineer II (Model Training)

The Bison Group LLC is seeking a Machine Learning Engineer II to lead model training for AI analysis tools, spanning dataset preparation, fine-tuning, evaluation, and deployment. This onsite role in El Paso, TX will focus on open-weight language, retrieval, and speech-related models, with an emphasis on domain adaptation and offline, reproducible training workflows.

Responsibilities

• Train and fine-tune open-weight language models using supervised fine-tuning and parameter-efficient methods such as LoRA/QLoRA.
• Improve model performance for 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 input.
• 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 multi-speaker conversations.
• Develop offline evaluation suites and regression tests covering citation and attribution accuracy, hallucination rate, and latency.
• Validate improvements against established baselines prior to quantization, export, and serving of trained models on on-premises GPUs and constrained hardware.
• Balance model quality with 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.

Requirements

• 3+ years of hands-on neural network training or fine-tuning.
• Ability to explain a project end to end, including data preparation, training decisions, evaluation results, and deployment.
• Hands-on fine-tuning of a 7B+ parameter open-weight language model using SFT, LoRA/QLoRA, or full fine-tuning, with PyTorch and Hugging Face Transformers/PEFT, NeMo, or equivalent tools.
• Dataset construction skills, including cleaning, deduplication, formatting, labeling of text data, and judgment on training suitability.
• Experience designing task-specific metrics and test sets, and diagnosing overfitting, catastrophic forgetting, and data leakage.
• GPU training fundamentals, including mixed precision, gradient accumulation, checkpointing, memory budgeting, and multi-GPU basics.
• Strong Python and Linux skills, including command-line work, CUDA environment management, and dependency management without internet access.
• U.S. citizenship and eligibility to obtain and maintain a U.S. security clearance.

Technologies

Python, Linux, CUDA, PyTorch, Hugging Face Transformers, PEFT, NeMo, LoRA, QLoRA, supervised fine-tuning (SFT), embedding models, reranker models, retrieval-augmented generation (RAG), speech recognition, speaker diarization, voice activity detection, Quantize, experiment tracking, model cards, on-premises GPUs, mixed precision, gradient accumulation, checkpointing, multi-GPU basics.

Preferred Qualifications

• iOS development, especially on-device machine learning with Core ML or MLX.
• Training small language models or distilling larger models for edge and on-device use.
• Experience with NVIDIA NeMo or model serving with NIM/TensorRT-LLM.
• Preference tuning with DPO, ORPO, or RLHF, or experience with reward modeling.
• Training embedding or reranker models for RAG applications.
• Speech recognition fine-tuning, speaker diarization, or voice activity detection.
• Quantization and inference tooling such as GGUF, llama.cpp, vLLM, AWQ/GPTQ, or MLX.
• Experience with ARM64 or other unified-memory platforms.
• Work in defense, intelligence, or air-gapped environments; an active Secret clearance or higher.
• Familiarity with qualitative research data, including focus groups, surveys, and interview materials.

Location and Experience

• Location: El Paso, TX (onsite)
• Minimum Experience: 3 years