Apply to Normal ComputingAbout The Role
We are seeking an experienced Sr. ML Engineer to join our team. In this role, you will be responsible for developing and implementing cutting-edge probabilistic machine learning models and algorithms that solve real-world problems for our clients. You will work closely with our research scientists, data scientists, and software engineers to deliver high-quality solutions that meet the needs of our clients.
Responsibilities:
Develop and implement state-of-the-art probabilistic machine learning models and algorithms to solve real-world problems.
Explore Bayesian and non-Bayesian approaches to Reliable Deep Learning: for example, uncertainty-aware algorithms for diverse and quality-filtered generations, adaptive or distilled algorithms for fast generations, and techniques for probing and model and training quality understanding
Work closely with the engineering team to build seamless features and integrations for core Probabilistic ML platform
Collaborate with research scientists and data scientists to develop new models and techniques for probabilistic machine learning..
Stay up-to-date with the latest research and industry trends in probabilistic machine learning.
Qualifications:
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 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.
Familiarity with probabilistic programming languages (e.g. Tensorflow Probability, Pyro)
Preferred qualifications:
Experience and participation in a team driving a project from – conception to an experimental idea – to a proof-of-concept – to a launched product feature.
Experience in cross functional collaboration, with research teams, product teams, and external partners and stakeholders
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.
Additional Information:
Normal Computing values diverse perspectives and experience. We encourage you to apply to this role if you feel you would be a good fit, even if you do not meet all of the requirements listed.
This role will receive a competitive salary + benefits + equity.
NY Est. Base Annual Range: $140,000-$200,000
A variety of factors are considered when determining someone’s compensation–including a candidate’s professional background, experience, and location. Final offer amount may vary from the amount listed above.
Normal Computing is an equal opportunity employer. We are committed to building a diverse and inclusive workforce and do not discriminate based on race, religion, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, age, sexual orientation, veteran or military status, or any other legally protected characteristics, Normal Computing is committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. To request a reasonable accommodation, please email accomodations@normalcomputing.ai
Normal is a New York-based deep-tech startup founded by former engineers from Google Brain, Alphabet X, and Palantir. Our investors include Celesta Capital, First Spark Ventures, Micron and former Google CEO Eric Schmidt. We engage with enterprise companies across various industries, including services, manufacturing, and the public sector, to deliver cutting-edge AI solutions.
We are on a mission to make AI universally scalable and useful.
Our products serve as critical full-stack infrastructure for our enterprise users in deploying AI into high-stakes applications. We are addressing the challenges of reliability, adaptivity, and auditability, which have traditionally been central barriers to adoption.
We believe that the untapped potential of AI to create transformative value remains to be fully realized. Thus far, AI has been subject to technological limitations such as unpredictable factual errors in generative AI (known as hallucinations) or lack of auditability as black-box models. Consequently, these limitations restrict the application of AI primarily to consumer-grade, low-stakes generative AI workflows and basic pattern recognition systems.
We envision that true transformative value can be unlocked in enterprise-grade, high-stakes AI workflows, where AI can reason reliably and autonomously, and understand its own limits. In these contexts, AI has the capacity to drive meaningful outcomes with real, complex impact.
Our approach involves redesigning AI systems from the ground up, contrasting other surface-level approaches. Our AI application development platform is powered by novel full-stack probabilistic machine learning infrastructure driven by thermodynamic physics. With Normal's probabilistic AI, we offer unprecedented control over reliability, adaptivity, and auditability to AI models, specifically tailored for critical and customer-specific enterprise workflows.
As we forge ahead with our mission, we are seeking passionate individuals eager to collaborate with our uniquely diverse and interdisciplinary team, and motivated by a workplace where the hardest problems remain to be solved.