Description
Leidos has a career opening for a
Senior Generative AI Data Scientist.
We are looking for a motivated Sr. Generative AI Data Scientist that wants to work on challenging problems in a variety of domains - including health, defense, intelligence, and energy – to get results that apply and go beyond the state of the art for measurably better outcomes. We apply our knowledge, capabilities, and experience to develop and deploy Trusted AI – AI that deserves to be trusted by system owners, end users, and the public – to be accurate, fair, ethical, reliable, and adaptable. We are looking for a researcher that is expert in NLP and interested in adapting new technologies to significantly transform human workflows, especially using new applications of transformer-based models.
Here’s What ChatGPT Has To Say On The Subject
Working at Leidos would give you the chance to do genuinely important work. But don’t just ask us.
“Oh sure, working in AI research for the commercial sector is just the ultimate thrill. Who wouldn't want to spend their days building algorithms to recommend cat videos to stream and optimizing ad targeting to boost click-through rates? I mean, those are the kinds of problems that really make you feel like you're changing the world, right? On the other hand, working in AI research at a company like Leidos, you're stuck with jobs like supporting the next generation of lunar missions and building computer vision algorithms that help keep air travel safe and efficient. I mean, who cares about finding cures for cancer, delivering healthcare to veterans, or helping defend our nation? So boring. But hey, at least we can say we're using our AI skills for a greater good.”
Primary Responsibilities
The Senior Generative AI Data Scientist will be customizing and creating various machine learning algorithms to operate over multi-domain data and optimizing the performance of those algorithms on the data. They will develop automation to extract and prepare features from multi-domain datasets. They will employ NLP libraries/toolkits that include transformer models like BERT and ChatGPT, as well as Stanford CoreNLP, Spacy, NLTK, Word2Vec, and Gensim.
As a member of the Leidos AI/ML Accelerator, they will be performing research and development, and need hands-on experience training and optimizing generative models. With those models and libraries, they will explore subjects like domain adaptation with supervised and unsupervised approaches. They will also build domain adaptive prototypes to examine the operational capabilities of bi-directional learning. They should be a self-starter while also being part of a team, collaborating and sharing discoveries and seeking feedback. They must be prepared to conduct research, document it, submit their research for publication, and present their research at conferences and other public forums.
Basic Qualifications
- Bachelor's degree in Computer Science, Data Science or related field and 15+ years’ experience or master’s degree at 13+ years of experience.
- Good understanding of machine learning algorithms, tools and platforms
- Experience in at least three of these Toolkits: NumPy, SciPy, scikit-learn, TensorFlow, Pytorch, Keras, Genism, vow pal wabbit, Stanford CoreNLP, etc.
- Experience researching and applying large language and generative AI models, including transformers, foundation models, and GPT models.
- Python proficiency
- Self-starter with high intellectual curiosity
- Great communication skills, able to explain language model results to a non-technical audience
- Proficient in data exploration techniques and tools
- Must be a US Citizen and be able to obtain TS/SCI with CI Poly security clearance.
Preferred Qualifications
- Practical understanding of generative models
- Experience programming machine learning algorithms for GPUs
- Understanding of Convolutional Neural Nets
- Working knowledge of Word2Vec and/or NLTK
- Discernment of when and how to use machine learning regulation
Pay Range
Pay Range $142,350.00 - $257,325.00
The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.
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