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Our client is a management consulting and technology firm focused on transforming global healthcare and diverse industries through data-driven strategies, cutting-edge analytics, and advanced AI solutions. This role sits within a focused research initiative dedicated to adapting small language models (SLMs) to specialized, high-value domain applications.
Requirements
Experience: 3+ years in Machine Learning Engineering or Applied ML, with hands-on experience fine-tuning transformer-based language models.
Core Tech Stack: Advanced Python and PyTorch proficiency.
Frameworks & Ecosystem: Deep fluency in the Hugging Face ecosystem, including Transformers, PEFT, Datasets, Accelerate, and TRL.
Fine-Tuning Expertise: Practical command of SFT with LoRA/QLoRA, alongside the judgment to reason about CPT and RL/preference methods.
Model Selection: Strong understanding of base model architectures, tokenizer design, pre-training data distributions, licensing, and context window requirements.
Data Engineering for ML: Proven experience building training pipelines (curation, cleaning, synthetic data generation, and formatting).
Evaluation & Serving: Ability to design executable correctness evaluations, paired with hands-on experience serving and quantizing models using at least one modern inference framework.
Research Fluency: Comfort reading recent ML research papers and translating state-of-the-art methods into working code.
Nice to have
Hands-on experience with Continued Pre-Training (CPT) on large domain corpora.
Experience with Reinforcement Learning / preference optimization for LLMs (reward modeling, DPO, GRPO, or verifiable-reward setups).
Experience with structured-output generation, code generation (e.g., SQL/DDL), or domain classification fine-tuning.
Familiarity with distributed training frameworks (DeepSpeed, FSDP).
Knowledge distillation experience transferring capabilities from large teacher models to small student models.
Experience with on-device or edge deployment.
Active open-source contributions to machine learning tooling, libraries, or published models.
Responsibilities
Own the Model Adaptation Lifecycle: Drive end-to-end adaptation of SLMs to specialized domain tasks, choosing the right base models, training strategies, and architectures.
Apply Efficient Fine-Tuning: Implement parameter-efficient fine-tuning (PEFT) methods with a mathematically grounded understanding of low-rank adaptation (LoRA, QLoRA, rank selection, parameterization, scaling, and accuracy–efficiency trade-offs).
Select Strategy per Task: Determine and sequence optimal adaptation strategies - including Continued Pre-Training (CPT), Supervised Fine-Tuning (SFT), and RL/preference tuning (DPO, GRPO, RLHF).
Curate & Synthesize Data: Clean, curate, format, and generate high-quality datasets, including synthetic data generation when domain data is scarce.
Design Functional Evaluations: Build task-specific benchmarks grounded in functional, executable correctness rather than loss curves or perplexity alone, maintaining regression tracking across versions.
Leverage Knowledge Transfer: Use larger models as teachers to transfer capabilities to SLMs via distillation, structured pruning, and quantization.
Deploy & Optimize Serving: Stand up and optimize inference infrastructure across GPU, CPU, and edge targets using frameworks such as vLLM, TGI, Ollama, https://jobeax.com/link/vmgbP67uWXPlLqsi, or TensorRT-LLM.
Pipeline Reproducibility & Research Execution: Break target tasks into milestones with clear criteria, maintaining reproducible pipelines with data/config versioning, checkpointing, and experiment tracking.
Translate recent literature into reproducible experiments.
Benefits
Employment-based cooperation
Comprehensive insurance for you and your family (health, life, and accident)
Paid PTO policy (vacation, sick leaves, and public holidays)
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