• Tony Nguyen
  • Senior AI Model Builder (Foundation Models & Scientific ML)

Senior AI Model Builder (Foundation Models & Scientific ML)

Engineering Python Machine Learning AI/Artificial Intelligence

Icon salary Mức lương
$3.000 - 5.000
Icon Location Khu vực
Hanoi

Phúc lợi

Lương tháng 13 Lương tháng 13
Phúc lợi khác Phúc lợi khác

Tổng quan về công việc và trách nhiệm

We are looking for a Senior Backend Engineer to help build and scale the backend engine of our drug-discovery Super Intelligence. You will: - Design and build the APIs, services, and data flows that power AI agents, models, and lab workflows. - Work closely with AI researchers, data scientists, and biologists to turn ideas into running systems. - Help shape how our PD and CNS foundation models are served, observed, and improved over time. - This is a hands-on role with high ownership. You will write code, make architecture decisions. KEY RESPONSIBILITIES 1. Lead PD-focused foundation model development - Own the strategy to fine-tune / distill LLMs into a PD-specialized model for scientific reasoning, evidence-grounded answers, and safe tool usage. - Design instruction and preference datasets, including curation, synthetic data generation, and quality gates. - Implement alignment techniques to reduce hallucinations and increase reliability in biomedical contexts. 2. Run serious experiments, not toy demos - Define benchmarks, offline evaluation suites, and rubrics (accuracy, calibration, robustness, hallucination rate, tool-call correctness). - Run ablations and deep error analysis; identify failure modes and drive targeted model/data fixes. - Set up continuous evaluation and regression checks to protect quality across model versions. 3. Ship models into production with engineers, scientists, and lab - Work with MLOps to package models for GPU inference, version them, and monitor them in production. - Define stable model interfaces (schemas, metadata, confidence fields, provenance hooks) so downstream services remain reliable. - Work with computational biologists to ensure model outputs make biological sense, not just statistical sense. - Incorporate feedback from lab experiments to update datasets, labels, and training strategies. - Maintain a clean experiment registry: configs, datasets, metrics, and reproducible training runs.

Kỹ năng và kinh nghiệm tối thiểu

- 4+ years working on machine learning / deep learning (industry, research lab, or strong personal projects). - Excellent fundamentals in deep learning, probability/statistics, and evaluation methodology. - Strong skills in Python and at least one major DL framework. - Hands-on experience training or fine-tuning large models (LLMs, vision models, graph models, or multimodal models). - Comfortable with data pipelines: cleaning, splitting, augmenting, and loading large datasets efficiently. - Able to read ML papers and turn ideas into running code and experiments. - Curious about biology and drug discovery, even if you are still learning the domain. - Enjoy working in a fast, messy, ambitious environment where specs are not always final, and you help design the answer.

Tại sao ứng viên nên làm ở đây

- Ownership of core intelligence: You’ll lead the modeling direction for Parkinson’s Disease-focused foundation models and scientific predictors that sit at the heart of our end-to-end discovery engine—not side experiments. - High-impact mission: Your work directly affects how quickly we can generate and prioritize better Parkinson’s Disease drug candidates, with real-world downstream validation. - GPU-first build environment: Access to modern GPU infrastructure and an engineering stack designed for training + high-throughput inference, with strong MLOps support for deployment. - End-to-end shipping culture: You’ll take models from idea → experiment → evaluation → production. We value measurable impact, reproducibility, and decision- grade reliability. - Annual leave - SHUI and Health Insurance - Bonuses and 13th-month salary. - Other benefits are considered on a case-by-case basis

Ưu tiên có kỹ năng và kinh nghiệm

- Experience with scientific ML: molecules (SMILES, graphs), proteins, or biomedical texts. - Familiarity with NVIDIA AI tools (BioNeMo, NeMo, Triton, NIM) or similar. - Experience with vector search and embeddings (pgvector, FAISS, Pinecone, Weaviate). - Built AI experiment pipelines with tracking tools (Weights & Biases, MLflow, custom dashboards). - Familiarity with knowledge graphs or graph neural networks (GNNs). - Publications, open-source contributions, or a strong project portfolio in ML/AI.

Báo cáo cho

Management team

Quá trình phỏng vấn

3 rounds: Phone screen -> Assignment -> Technical round

Tony Nguyen

Headhunter | Recruiter
Verified
employee 7057 ứng viên
cup 857 phỏng vấn
health 59 đi làm

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Tony Nguyen

Headhunter | Recruiter
Verified
Icon employee 7057 ứng viên
Icon cup 857 phỏng vấn
Icon health 59 đi làm
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