**Job Opportunity: Senior Machine Learning Engineer
Location:**
Remote / Hybrid across US and Europe
We are seeking a
Senior Machine Learning Engineer
to join a leading Machine Learning Science team within a computational biology research environment. This role is ideal for a hands-on engineer experienced in building scalable, distributed deep learning pipelines and AI/ML systems in cloud environments. You will enable the development of state-of-the-art DL models on large, complex datasets, collaborating closely with ML scientists, computational biologists, and software engineers.
What You’ll Do
- Design, implement, and optimize distributed deep learning pipelines for training, inference, and data handling.
- Collaborate with ML scientists and software engineers to align pipelines with research objectives.
- Monitor, evaluate, and improve pipeline performance and scalability.
- Maintain robust, reproducible DL workflows for consistent and accurate results.
- Drive efficiency improvements through profiling, caching, and debugging distributed systems.
- Act as a technical bridge between engineering and scientific teams, documenting best practices and fostering a culture of continuous improvement.
- Stay current with AI/ML advancements and rapidly integrate new tools and frameworks.
Must-Have Qualifications
- MS or equivalent experience in Computer Science, Statistics, Mathematics, Software Engineering, or related fields, with AI/ML emphasis.
- 5+ years of industry experience in developing AI/ML software engineering pipelines.
- Proficiency in Python (preferred), Java, C/C++, Julia, or similar languages.
- Hands-on experience with ML/DL frameworks: PyTorch, TensorFlow, JAX, or Scikit-learn.
- Expertise in scalable and distributed computing platforms (e.g., Ray, DeepSpeed) and ML developer tools (TensorBoard, WandB, MLflow).
- Experience with cloud platforms (AWS, GCP, Azure) and deploying ML/AI pipelines in cloud environments.
- Knowledge of containerization (Docker) and orchestration tools (Kubernetes) for scalable ML solutions.
- Experience managing large datasets and optimizing high-complexity data workflows.
- Proficiency with version control (Git) and CI/CD practices.
- Strong communication skills and ability to collaborate across disciplines.
Nice-to-Have
- Experience with large-scale genomics or biological datasets.
- Experience with multimodal datasets (sequence, text, image, etc.).
- GPU/Accelerator programming and kernel development (CUDA, Triton, XLA).
- Infrastructure-as-code and ML infrastructure best practices.
- Contributions to relevant DL projects (e.g., GitHub).
If you are passionate about building cutting-edge ML infrastructure and want to support high-impact scientific research, this is an exceptional opportunity to make a real-world impact.