Machine Learning Engineer - MLOps & AI Platform

We are seeking a hands-on Machine Learning Engineer to join the Biologics AI CoE. This is primarily an ML engineering role with meaningful ownership of MLOps and GPU-compute enablement. The successful candidate will turn research code into reliable, reproducible, and scalable training and inference workflows for biologics AI applications.

Working closely with ML scientists, computational biologists, and infrastructure partners, this individual will improve engineering quality, developer experience, and compute efficiency across the model lifecycle. The role includes practical support for SLURM-based GPU workloads, experiment tracking, profiling, source control, testing, and CI/CD.

Essential Responsibilities:

• Engineer and maintain reusable training, evaluation, and inference pipelines for deep-learning models used in biologics discovery.

• Translate research prototypes into well-tested, modular, documented, and maintainable production-quality code.

• Enable efficient single-node and distributed GPU training using PyTorch DDP/FSDP, DeepSpeed, or comparable frameworks.

• Profile model training and inference to identify bottlenecks in GPU utilization, memory, data loading, kernels, and interconnect communication; implement and validate improvements.

• Support day-to-day use of SLURM-based GPU clusters, including job templates, queues/partitions, resource requests, failure diagnosis, and user guidance; partner with IT/HPC teams on underlying infrastructure issues.

• Build dashboards, reports, or alerts for GPU utilization, job efficiency, capacity trends, and failed or stalled workloads.

• Establish reproducible experiment-management practices using Weights & Biases (W&B) or equivalent tools, including run metadata, artifacts, model versions, and comparisons.

• Own and promote GitHub engineering practices: branching, pull requests, peer review, code ownership, issue tracking, release practices, and repository hygiene.

• Develop and maintain automated tests and CI/CD workflows for ML code, containers, and deployment artifacts.

• Create containerized, reproducible runtime environments and manage dependencies for on-premises and/or cloud execution.

• Implement model packaging and serving workflows for batch and online inference, with appropriate monitoring for latency, throughput, reliability, and data/model drift where applicable.

• Document platform standards, troubleshooting playbooks, and self-service workflows; coach scientists on efficient and responsible use of shared compute resources.

• Evaluate new ML systems and MLOps technologies pragmatically, balancing scientific velocity, reliability, security, maintainability, and cost.

Qualifications Required:

• Bachelor's or Master's degree in a relevant quantitative or engineering discipline, with typically 4+ years of relevant industry experience; equivalent practical experience will be considered.

• Strong Python engineering skills and hands-on experience with PyTorch or another modern deep-learning framework.

• Demonstrated ability to build, debug, and optimize ML training and inference pipelines on NVIDIA GPUs.

• Working knowledge of Linux, Bash, networking/storage fundamentals, environment and dependency management, and systematic troubleshooting.

• Hands-on experience with SLURM or a comparable workload scheduler in an HPC or multi-GPU environment.

• Experience with GPU and application profiling tools such as PyTorch Profiler, NVIDIA Nsight Systems/Compute, nvidia-smi/DCGM, or equivalent tooling.

• Practical experience with Git and GitHub, pull-request review, automated testing, and CI/CD (for example, GitHub Actions).

• Experience with experiment tracking and artifact/model management using W&B, MLflow, or a comparable platform.

• Experience with Docker or another container technology; familiarity with Kubernetes is beneficial but not mandatory.

• Understanding of distributed training, mixed precision, checkpointing, reproducibility, and performance/cost trade-offs.

• Strong ownership, communication, and collaboration skills, including the ability to work effectively with both research and infrastructure teams.

• Professional working proficiency in English; Chinese communication capability is strongly preferred for the Shanghai-based team.

Preferred Qualifications:

• Experience with protein language models, large language models, diffusion/flow models, graph neural networks, or other foundation-model workloads.

• Experience in computational biology, bioinformatics, biologics, pharmaceutical R&D, or another scientific-computing environment.

• Familiarity with Azure, AWS, or GCP; infrastructure-as-code or configuration-management experience is a plus.

• Experience with model-serving technologies such as NVIDIA Triton, vLLM, TorchServe, or KServe.

• Experience building internal ML platforms, developer tooling, observability, or chargeback/capacity-planning solutions.