[All remote jobs](https://jobicy.com/jobs.md)[![Deepgram logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2020/12/WRILS-201212160008-406229.jpg)](https://jobicy.com/company/deepgram.md)[Deepgram](https://jobicy.com/company/deepgram.md) # Research Engineer, Machine Learning Systems Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/deepgram.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 150k–250k / yrDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceSeniorPublished29 Oct 2026Apply before28 Nov 2026Listing views60Application actions4Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Deepgram is hiring a Research Engineer for Machine Learning Systems to accelerate research and production deployment of speech-to-text and text-to-speech models. The role combines ML experimentation with scalable distributed training, data infrastructure, orchestration, evaluation, and internal tooling. The engineer will partner closely with research scientists to validate novel modeling ideas quickly and scale successful prototypes into robust systems. Strong experience with speech-related ML workflows, unstructured audio and text data, Kubernetes, Docker, Prefect, MLflow, and stakeholder-facing tooling is expected. The environment emphasizes rapid experimentation, AI-enabled productivity, ownership, and adaptability rather than a prescriptive 9-to-5 workflow. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a senior-level research engineering role spanning distributed ML systems, speech-model development, data engineering, and internal product tooling. Success requires independently selecting high-leverage experiments and scaling research prototypes into reliable, high-throughput training infrastructure. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$200,000US market range (per year)$180k–$280k0$308k AI insightThe employer discloses a base compensation range of $150,000–$250,000 USD per year, with a midpoint of $200,000. The estimated US annual market range for a research-focused ML systems engineer with speech, distributed-training, and infrastructure expertise is $180,000–$280,000 USD; this is an AI estimate, not verified salary research. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Speech Recognition](https://jobicy.com/jobs?search_keywords=Speech%20Recognition.md)[Text-to-Speech](https://jobicy.com/jobs?search_keywords=Text-to-Speech.md)[Distributed Training](https://jobicy.com/jobs?search_keywords=Distributed%20Training.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[Docker](https://jobicy.com/jobs?search_keywords=Docker.md)[Prefect](https://jobicy.com/jobs?search_keywords=Prefect.md)[MLflow](https://jobicy.com/jobs?search_keywords=MLflow.md)[Data Engineering](https://jobicy.com/jobs?search_keywords=Data%20Engineering.md)[Audio Data Processing](https://jobicy.com/jobs?search_keywords=Audio%20Data%20Processing.md)[Experiment Tracking](https://jobicy.com/jobs?search_keywords=Experiment%20Tracking.md)[Model Evaluation](https://jobicy.com/jobs?search_keywords=Model%20Evaluation.md)[Internal Tooling](https://jobicy.com/jobs?search_keywords=Internal%20Tooling.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md) Sample interview questionsHow would you identify the most valuable bottleneck to address in an end-to-end speech-model training workflow?I would first instrument the workflow across data preparation, storage access, scheduling, GPU utilization, training throughput, checkpointing, and evaluation. I would quantify wall-clock cost and failure rates for each stage, then prioritize the bottleneck with the greatest impact on researcher iteration time and compute efficiency. I would validate the proposed improvement with a narrowly scoped experiment before investing in a broader platform change. Describe how you would design a scalable training platform for STT and TTS research. I would separate reproducible dataset versioning, configurable training jobs, distributed compute orchestration, experiment tracking, artifact storage, and automated evaluation into clear components. Kubernetes would provide elastic scheduling, while containerized workloads and declarative configurations would make experiments reproducible. The platform should expose simple interfaces for researchers while preserving observability, resource controls, and lineage for engineering teams. What challenges are unique to data engineering for large-scale audio datasets, and how would you address them? Audio introduces large storage requirements, inconsistent formats, variable sampling rates, noisy labels, privacy concerns, and expensive preprocessing. I would use standardized representations, metadata-rich manifests, distributed preprocessing, quality validation, deduplication, and versioned dataset snapshots. I would also optimize data locality and streaming so accelerators are not idle while waiting for audio features or text labels. How would you make ML tooling useful to non-technical stakeholders without oversimplifying critical information? I would begin with the decisions each audience needs to make, then present clear task-oriented views such as experiment status, evaluation comparisons, dataset health, and model-release readiness. Advanced details should remain discoverable through drill-downs rather than dominating the primary interface. I would validate the UI with users iteratively and include definitions, guardrails, and permissions to prevent misinterpretation or unsafe actions. Tell us about a time you had to decide quickly whether to continue or stop a technical research direction. I would define an explicit hypothesis, the minimum experiment that can test it, and quantitative success and failure thresholds before implementation. After running the experiment, I would compare outcomes against the baseline and inspect failure modes rather than relying only on aggregate metrics. If the result did not meet the threshold, I would document the finding, stop or revise the approach promptly, and redirect resources to the next highest-value hypothesis. Opportunity details ## About this role. ### Company Overview Deepgram 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 Rhythm At 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 Opportunity Voice 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 Role Deepgram 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 Challenge We 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 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. ### 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](http://deepgram.com) email address. If you’ve received a message claiming to be Deepgram, please forward it to careers@deepgram.com. 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