SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Immunai is an AI-driven platform company focused on improving drug discovery and development by decoding the human immune system. The company combines large-scale single-cell immune data, advanced machine learning, and strong engineering to help pharmaceutical and research partners make better decisions throughout the drug development process.
You will join as a Senior ML Infrastructure Engineer, playing a central role in evaluating and deploying Immunai's AI models. This role sits at the intersection of software engineering, model understanding, and applied AI, with broad influence across the organization. This is not a core algorithmic research position, but rather a role focused on building ML engineering infrastructure for model evaluation and deployment.
Key responsibilities:
- Own and evolve Immunai's benchmarking suite for foundation models and multimodal AI systems
- Design, build, and maintain clean, extensible abstractions and APIs for datasets, tasks, models, metrics, and evaluation workflows
- Implement metrics that capture predictive performance, biological relevance, and multimodal alignment
- Work closely with AI scientists and data scientists to integrate new models, tweak architectures, and enable rapid, fair iteration
- Add external and internal models to benchmarks and ensure meaningful comparisons
- Dive into data and results to debug evaluations, understand model behavior, and unblock modeling work
- Ensure evaluations are consistent, well-versioned, and trustworthy over time
You will have broad influence on how models are built, compared, and improved across the organization, supporting the long-term goal of reducing drug development failure rates and helping more effective medicines reach patients.
REQUIREMENTS:
- BSc, MSc, or PhD in Computer Science, Software Engineering, or related field
- 5+ years hands-on, industry experience working with ML models and evaluation pipelines (required)
- Strong software engineering skills with experience designing maintainable, modular systems
- Proficiency in Python and modern ML ecosystems
- Ability to read, modify, and debug deep learning models
- Experience with benchmarks, metrics, or evaluation frameworks (preferred)
- Familiarity with foundation models or multimodal learning (preferred)
- Comfort navigating complex datasets and doing targeted exploratory analysis
- Experience in biomedical or other data-intensive domains (a plus)
