Normal Computing. Incredible Opportunities.

At Normal, our mission is to make AI universally scalable and useful. We believe that AI can unlock transformative value in enterprise applications by reasoning reliably, autonomously, and understanding its own limits. Our products enable AI deployment in high-stakes enterprise applications.

We understand that our technology is only as powerful as the people behind it. Our team members are driven by curiosity and passion for tackling the world’s most challenging problems. At Normal, every employee has the chance to make a significant impact, and build a career that is anything but Normal!

Your Role in Our Mission

As an ML Infrastructure Engineer at Normal Computing, you will play a crucial role in shaping our full-stack AI platform by designing, building, and maintaining scalable machine learning infrastructure for the development, training, and deployment of frontier machine learning techniques and algorithms.

Depending on interest and skills, responsibilities could include:

• Collaborating closely with ML research scientists and engineers to optimize and productionize pipelines and workflows, ensuring efficiency, best practices, and effective resource utilization.

• Implementing tools, libraries and frameworks to speed up and enable new research.

• Collaborate closely with Thermodynamic Hardware scientists and engineers to integrate a novel simulation stack and compilation engine

• Be a part of planning and performing rapid prototyping of machine learning techniques applied to real-world scientific and enterprise problems.

• Make improvements to model architectures, training, simulation, and compilation procedures

• Report and present software developments, experimental results and analysis clearly and efficiently.

• Contributing to existing documentation or educational content (e.g. blog posts or talks) and adapt content based on product/program updates and user feedback

• Stay up-to-date with the latest industry trends and technologies, driving continuous improvement and innovation within our ML platform.

• Mentoring and guiding junior colleagues, nurturing a collaborative, growth-oriented environment that promotes knowledge sharing and professional development.

What Makes You A Great Fit:

• Bachelor’s degree or higher in Computer Science, Engineering, or a related field.  

• 3+ years of experience in infrastructure engineering, with a focus on machine learning, distributed systems, and cloud computing.

• Experience with at least one programming language (preference for those commonly used in ML or scientific computing such as Python or C++).

• Experience using TensorFlow, PyTorch, Jax, NumPy, Pandas or similar ML/scientific libraries.

• Leadership and collaboration qualities, enthusiasm for real-world, responsible impact

• Excellent problem-solving skills and a proven ability to troubleshoot and optimize complex systems.

• Strong written and verbal communication skills, with the ability to explain complex concepts to both technical and non-technical stakeholders.

Preferred qualifications:

• Knowledge of containerization technologies (Docker, Kubernetes) and any cloud platform like GCP, AWS, Azure.

• Familiarity with ML infrastructure tools and technologies, such as Ray, MLflow, Kubeflow, Flyte, or similar platforms.

• Strong understanding of CI/CD pipelines, infrastructure-as-code (Terraform, CloudFormation), and configuration management tools (Ansible, Puppet, Chef).

• Experience with big data technologies such as Hadoop, Spark, or Flink.

• Familiarity with data storage and processing systems (SQL/NoSQL, Kafka).

• A passion for staying up-to-date with the latest advancements in AI, ML, and infrastructure technologies.

What Elevates Your Application:

• Applied experience with machine learning, preferably modern deep learning architectures (e.g. Transformers, CNNs, vision-language models, deep reinforcement learning)·      

• Experience with machine learning training objectives beyond accuracy (e.g. Bayesian learning, meta-learning, value-at-risk, robustness, distribution shift, class imbalance, fairness)

• Experience with large-scale Bayesian modeling and inference

• Comfort with probabilistic programming languages (e.g. Tensorflow Probability)

• Experience in cross functional collaboration, with research teams and product teams.

Equal Employment Opportunity Statement

Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.

Accessibility Accommodations

Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at [email protected].

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