• Your work sits directly at the intersection of AI research and scalable software engineering. You will define how natural, fast, and reliable our models perform in production. • Research & Train: Design, train, and evaluate our proprietary deep learning models. You will drive the development of new capabilities, such as emotion recognition, and integrate them into our core architecture. • High-Performance ML Systems: Optimize our models for maximum inference speed and efficiency, ensuring they can handle massive datasets and real-time workloads at scale. • Software Engineering: Write clean, production-ready code. We need researchers who are also strong software engineers, capable of translating complex research into highly available architectures. • MLOps & Tooling: Build and maintain the internal pipelines for data processing, model training, and evaluation to make our iteration cycles fast and reliable.

• Contribution to research into 5G/6G research projects (RAN related) • Design, validation of RAN related algorithms/architectures (e.g. RRM) • Development of simulations (or prototypes) related to 5G/6G • Responsibility for selected tasks in EU projects • Writing reports, papers, contributing to patents • Good command of Python or Matlab scripting is required • Knowledge of C/C++/Java will be a plus

Job Description Role Summary At Citadel Securities, a leading global market maker, our team of Quantitative Developers and Research Engineers partner with Quantitative Researchers to create and implement automated trading system software solutions that leverage sophisticated statistical techniques and technologies. Opportunities available in Miami, Chicago, London, New York, Hong Kong, Sydney, Singapore Objectives - Design, develop, test, and deploy elegant software solutions for automated trading systems - Partner with the Quantitative Research team to define priorities and deliver custom software solution Skills and Preferred Qualifications - A deep passion for technology, software development, and mathematics - Proficiency within one or more programming languages, including C++, Python, and R - Experience with some of the following areas: Distributed Computing, Natural Language Processing, Machine Learning, Platform Development, Networking, System Design, and/or Web Development - Exceptional quantitative and analytical skills - Bachelor's, Master's or PhD degree in Computer Science, Mathematics, Statistics, or equivalent experience - Strong written and verbal communications skills In accordance with applicable law, the base salary range for this role is $250,000 to $350,000. In addition, the employee who fills this role will be eligible to participate in a discretionary incentive compensation program, as well as a wide array of benefit programs, such as medical and life insurance, retirement and tax-free savings plans, and access to other healthcare programs. About Citadel Securities Citadel Securities is a technology-driven, next-generation global market maker. We provide institutional and retail investors with world-class liquidity, competitive pricing and seamless front-to-back execution in a broad array of financial products. Our teams of engineers, traders and researchers harness leading-edge quantitative research and the accelerating power of compute, machine learning and AI to power our analytics and tackle the market's and our clients' most critical challenges. Together, we are forging the future of capital markets. For more information, visit citadelsecurities.com .

Company OverviewDeepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram. Company Operating RhythmAt Deepgram, we expect an AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance. Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do. Additionally, we move at the pace of AI. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5. The OpportunityVoice is the most natural modality for human interaction with machines. However, current sequence modeling paradigms based on jointly scaling model and data cannot deliver voice AI capable of universal human interaction. The challenges are rooted in fundamental data problems posed by audio: real-world audio data is scarce and enormously diverse, spanning a vast space of voices, speaking styles, and acoustic conditions. Even if billions of hours of audio were accessible, its inherent high dimensionality creates computational and storage costs that make training and deployment prohibitively expensive at world scale. We believe that entirely new paradigms for audio AI are needed to overcome these challenges and make voice interaction accessible to everyone. The RoleDeepgram is currently looking for an experienced researcher to who has worked extensively with Large Language Models (LLMS) and has a deep understanding of transformer architecture to join our Research Staff. As a Member of the Research Staff, this individual should have extensive experience working on the hard technical aspects of LLMs, such as data curation, distributed large-scale training, optimization of transformer architecture, and Reinforcement Learning (RL) training. The ChallengeWe are seeking researchers who: - See "unsolved" problems as opportunities to pioneer entirely new approaches - Can identify the one critical experiment that will validate or kill an idea in days, not months - Have the vision to scale successful proofs-of-concept 100x - Are obsessed with using AI to automate and amplify your own impact If you find yourself energized rather than daunted by these expectations—if you're already thinking about five ideas to try while reading this—you might be the researcher we need. This role demands obsession with the problems, creativity in approach, and relentless drive toward elegant, scalable solutions. The technical challenges are immense, but the potential impact is transformative. What You'll Do - Brainstorming and collaborating with other members of the Research Staff to define new LLM research initiatives - Broad surveying of literature, evaluating, classifying, and distilling current methods - Designing and carrying out experimental programs for LLMs - Driving transformer (LLM) training jobs successfully on distributed compute infrastructure and deploying new models into production - Documenting and presenting results and complex technical concepts clearly for a target audience - Staying up to date with the latest advances in deep learning and LLMs, with a particular eye towards their implications and applications within our products You'll Love This Role if You - Are passionate about AI and excited about working on state of the art LLM research - Have an interest in producing and applying new science to help us develop and deploy large language models - Enjoy building from the ground up and love to create new systems. - Have strong communication skills and are able to translate complex concepts clearly - Are highly analytical and enjoy delving into detailed analyses when necessary It's Important to Us That You Have - 3+ years of experience in applied deep learning research, with a solid understanding toward the applications and implications of different neural network types, architectures, and loss mechanism - Proven experience working with large language models (LLMs) - including experience with data curation, distributed large-scale training, optimization of transformer architecture, and RL Learning - Strong experience coding in Python and working with Pytorch - Experience with various transformer architectures (auto-regressive, sequence-to-sequence.etc) - Experience with distributed computing and large-scale data processing - Prior experience in conducting experimental programs and using results to optimize models It Would Be Great if You Had - Deep understanding of transformers, causal LMs, and their underlying architecture - Understanding of distributed training and distributed inference schemes for LLMs - Familiarity with RLHF labeling and training pipelines - Up-to-date knowledge of recent LLM techniques and developments The ChallengeWe are seeking researchers who: - See "unsolved" problems as opportunities to pioneer entirely new approaches - Can identify the one critical experiment that will validate or kill an idea in days, not months - Have the vision to scale successful proofs-of-concept 100x - Are obsessed with using AI to automate and amplify your own impact If you find yourself energized rather than daunted by these expectations—if you're already thinking about five ideas to try while reading this—you might be the researcher we need. This role demands obsession with the problems, creativity in approach, and relentless drive toward elegant, scalable solutions. The technical challenges are immense, but the potential impact is transformative. What You'll Do - Brainstorming and collaborating with other members of the Research Staff to define new LLM research initiatives - Broad surveying of literature, evaluating, classifying, and distilling current methods - Designing and carrying out experimental programs for LLMs - Driving transformer (LLM) training jobs successfully on distributed compute infrastructure and deploying new models into production - Documenting and presenting results and complex technical concepts clearly for a target audience - Staying up to date with the latest advances in deep learning and LLMs, with a particular eye towards their implications and applications within our products You'll Love This Role if You - Are passionate about AI and excited about working on state of the art LLM research - Have an interest in producing and applying new science to help us develop and deploy large language models - Enjoy building from the ground up and love to create new systems. - Have strong communication skills and are able to translate complex concepts clearly - Are highly analytical and enjoy delving into detailed analyses when necessary It's Important to Us That You Have - 3+ years of experience in applied deep learning research, with a solid understanding toward the applications and implications of different neural network types, architectures, and loss mechanism - Proven experience working with large language models (LLMs) - including experience with data curation, distributed large-scale training, optimization of transformer architecture, and RL Learning - Strong experience coding in Python and working with Pytorch - Experience with various transformer architectures (auto-regressive, sequence-to-sequence.etc) - Experience with distributed computing and large-scale data processing - Prior experience in conducting experimental programs and using results to optimize models It Would Be Great if You Had - Deep understanding of transformers, causal LMs, and their underlying architecture - Understanding of distributed training and distributed inference schemes for LLMs - Familiarity with RLHF labeling and training pipelines - Up-to-date knowledge of recent LLM techniques and developments - Published papers in Deep Learning Research, particularly related to LLMs and deep neural networks - Published papers in Deep Learning Research, particularly related to LLMs and deep neural networks Benefits & Perks* Holistic health - Medical, dental, vision benefits - Annual wellness stipend - Mental health support - Life, STD, LTD Income Insurance Plans Work/life blend - Unlimited PTO - Generous paid parental leave - Flexible schedule - 12 Paid US company holidays - Quarterly personal productivity stipend - One-time stipend for home office upgrades - 401(k) plan with company match - Tax Savings Programs Continuous learning - Learning / Education stipend - Participation in talks and conferences - Employee Resource Groups - AI enablement workshops / sessions *For candidates outside of the US, we use an Employer of Record model in many countries, which means benefits are administered locally and governed by country-specific regulations. Because of this, benefits will differ by region — in some cases international employees receive benefits US employees do not, and vice versa. As we scale, we will continue to evaluate where we can create more alignment, but a 1:1 global benefits structure is not always legally or operationally possible. Backed by prominent investors including Y Combinator, Madrona, Tiger Global, Wing VC and NVIDIA, Deepgram has raised over $215M in total funding. If you're looking to work on cutting-edge technology and make a significant impact in the AI industry, we'd love to hear from you! Deepgram is an equal opportunity employer. We want all voices and perspectives represented in our workforce. We are a curious bunch focused on collaboration and doing the right thing. We put our customers first, grow together and move quickly. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, gender identity or expression, age, marital status, veteran status, disability status, pregnancy, parental status, genetic information, political affiliation, or any other status protected by the laws or regulations in the locations where we operate. We are happy to provide accommodations for applicants who need them.