Staff Deep Learning Engineer

Staff Deep Learning Engineer

Posted: June 18, 2025 Job Type: Permanent Industry: Development and Engineering

Our client, an innovative Silicon Valley-based biotech startup, is at the forefront of pioneering AI-driven models to simulate and program complex biological systems. With strategically located global hubs spanning Europe and the Middle East, this forward-thinking company seamlessly blends cutting-edge machine learning techniques with deep molecular biology insights to significantly accelerate breakthroughs in medicine and the life sciences.

They’re looking to expand their dynamic teams by bringing on a Staff Deep Learning Engineer who can drive innovation and contribute to groundbreaking research and development.

Location & Compensation:

• Location: Palo Alto, CA (Onsite)
• Salary: USD $170,000 – $210,000 annually

What You’ll Be Doing:

As a Staff Deep Learning Engineer, you will be a pivotal technical contributor, responsible for advancing the core AI models that power our client’s biological simulations. Your responsibilities will likely include:

• Model Development & Optimization: Designing, developing, and optimizing advanced deep learning models tailored for complex biological simulations and programming.
• GPU Accelerated Computing: Leveraging GPU tools such as CUDA and cuDNN to build high-performance computing solutions for deep learning workloads.
• Library & Framework Utilization: Working extensively with leading deep learning libraries like PyTorch and HuggingFace to implement and refine neural network architectures.
• Distributed Systems & Scalability: Contributing to the development and scaling of distributed deep learning systems, ensuring models can be trained and deployed efficiently across large datasets and computational resources.
• Backend & Database Integration: Engaging in robust back-end development using frameworks like Django, Flask, and Node.js, and integrating with databases such as PostgreSQL and MongoDB to manage and serve model data.
• Containerization & Orchestration: Utilizing containerization technologies (e.g., Docker) and orchestration platforms (e.g., Kubernetes, SLURM) to ensure reproducible and scalable deployment of models and related services.
• Cloud Platform Integration: Working with major cloud platforms such as AWS and GCP for deploying and managing deep learning infrastructure.
• Collaboration & Innovation: Collaborating closely with molecular biologists, data scientists, and other engineers to translate biological challenges into AI solutions and push the boundaries of scientific discovery.

Qualifications:

To be successful in this role, you should possess a strong blend of technical expertise and practical experience:

• Education: A Bachelor’s degree in Computer Science, Engineering, or a closely related field. Experience in life sciences is a significant plus.
• Programming Proficiency: Proficient in JavaScript, Python, and modern web frameworks relevant to deep learning and backend services.
• GPU & Deep Learning Libraries: Demonstrated experience with GPU tools (CUDA, cuDNN) and a strong command of deep learning libraries (PyTorch, HuggingFace).
• Orchestration & Cloud: Familiarity with SLURM, Kubernetes, and major cloud platforms (AWS, GCP).
• Backend & Database Skills: Skilled in back-end development using frameworks like Django, Flask, Node.js, and experience with databases such as PostgreSQL, MongoDB.
• Distributed Systems & Containerization: A strong background in designing and implementing distributed systems and utilizing containerization technologies (Docker, Kubernetes).

Preferred Qualifications:

Candidates with the following qualifications will be highly regarded:

• Advanced Degree: A Ph.D. in Computer Science, Engineering, or a related field; continued experience in life sciences is a plus.
• Large Language/Foundation Models: Experience with large language models (LLMs) or other foundation models and complex deep learning workflows.
• Biological Data & Bioinformatics: Familiarity with biological data, experience with bioinformatics tools, and understanding of ML pipelines in a life sciences context.
• Development Practices: Proficient in using Git for version control, experience with CI/CD pipelines for automated deployment, and designing/consuming RESTful APIs.

This is a permanent opportunity to join a pioneering biotech startup where your deep learning expertise will directly contribute to accelerating scientific breakthroughs. If you are passionate about the intersection of AI and life sciences, we encourage you to apply!