Research Engineer, Machine Learning Systems

Devconnectplatform; United States; A distancia; USD 150.000 - 250.000; Jornada completa

Descripción de la vacante

Deepgram is seeking a senior Research Engineer to design and scale end-to-end training systems for speech-to-text and text-to-speech models. You will collaborate with researchers to prototype novel ideas and develop robust, scalable pipelines and internal tools that enable non-technical stakeholders to access ML workflows.

As a member of the Research Staff, you’ll help accelerate experimentation, evaluate models, and translate proofs-of-concept into production-ready solutions, while working with

Formación

• Experience with ML research pipelines in STT or related speech domains.
• Proficiency with orchestration and infrastructure tools (Kubernetes, Docker, Prefect).
• Familiarity with ML lifecycle tools (MLflow).

Responsabilidades

• Architect scalable training systems for STT/TTS models.
• Build internal tools and dashboards for non-technical stakeholders.
• Collaborate with researchers, engineers and product teams to deploy ML research to production.

Conocimientos

Kubernetes

Docker

Prefect

MLflow

Data engineering

Speech tech

Distributed training

Herramientas

Kubernetes

Docker

Prefect

MLflow

Descripción del empleo

# Research Engineer, Machine Learning SystemsDeepgram150k–250k USDFull-timeremote· seniorUSAgokubernetesdockerListed via Jobicy · posted 29 Oct 2026. The application happens there, not on DevConnect.Apply on Jobicy## DescriptionCompany 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 seeking a highly skilled and versatile Machine Learning Engineer to join our Research team. As a Member of the Research Staff, you will partner with research scientists to prototype and validate novel modeling ideas, then scale them through robust training systems for speech technologies, internal tooling, and innovative data strategies. You'll work at the intersection of machine learning, data infrastructure, and internal tooling to support our mission of building world-class speech recognition and synthesis systems. On the Research team, you will experiment with new technologies and techniques, while also working on product-focused deliverables, learning from colleagues with a wide range of expertise in AI and machine learning as you go.Key Responsibilities-Scalable Model Training: Architect and manage horizontally scalable systems that dramatically accelerate the end-to-end training lifecycle for Speech-to-Text (STT) and Text-to-Speech (TTS) models. This includes far more than automated training: the role focuses on making model development significantly faster and more efficient through optimized data preparation and management, high-throughput training pipelines, distributed infrastructure, and automated evaluation tooling.-Tooling & Accessibility: Design and implement internal UIs and tools that make ML systems and workflows accessible to non-technical stakeholders across the company. These UIs should be designed to provide transparency and flexibility to internally built tooling.-Infrastructure & Tools: Oversee and manage training tooling, job orchestration, experiment tracking, and data storage.The ChallengeWe are seeking Members of the Research Staff 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 impactIf 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.It's Important to Us That You Have-Strong experience with the machine learning research pipeline, particularly in STT or related speech domains. This includes experimenting with and evaluating new architectures and modeling approaches, and implementing large-scale training systems.-Proficiency with orchestration and infrastructure tools like Kubernetes, Docker, and Prefect.-Familiarity with ML lifecycle tools such as MLflow.-Experience building internal tools or dashboards for non-technical users.-Hands-on experience with data engineering practices for unstructured audio and text data.-Comfortable working in cross-functional teams that include researchers, engineers, and product stakeholders.Why Join Deepgram?At Deepgram, you’ll help shape the future of human–machine communication. Our research culture prioritizes ownership, experimentation, and real-world impact. As a Member of the Research Staff, you'll be empowered to build tools and systems that accelerate ML research and product deployment at scale.Notice: We're aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @deepgram.com email address. If you've received a message claiming to be Deepgram, please forward it to [e-mail].