[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 Staff, Applied Voice AI AI Summary Deepgram is seeking a senior applied voice AI researcher to improve production speech and voice models against concrete customer problems. The role combines model evaluation, dataset curation, fine-tuning, and rapid delivery of reliable systems to production. Candidates need deep learning expertise, strong Python and PyTorch skills, and demonstrated ownership of end-to-end ML outcomes. Speech AI, distributed training, public research, benchmarks, and open-source contributions are valuable differentiators.[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 216k–270k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished11 Nov 2026Apply before11 Dec 2026Listing views47Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. Opportunity details ## About this role. ### Company Overview [Deepgram](https://jobicy.com/company/deepgram.md) is bringing the world’s most natural interface, voice, to the technology we use every day. Our speech models and real-time infrastructure power AI systems that keep up with the way people actually speak. More than 5,000 organizations run conversational voice AI on Deepgram in production, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Cresta, Granola, and Jack in the Box. The Deepgram Voice Agent Platform brings together speech-to-text, text-to-speech and speech-to-speech, with voice-native foundation models available through cloud APIs or self-hosted and on-premises deployments. Built for accuracy, low latency and cost efficiency, Deepgram makes high-quality voice experiences practical to build and run at scale. More than 650,000 developers have used Deepgram to process more than 200,000 years of audio. Every day, more of the world’s conversations with AI run on our models. Operating Principles: Deepgram is a research company that productizes as fast as possible. Four Operating Principles shape how we think, decide, and work together: * Compress before you communicate. You think it through first, then lead with the bottom line. * Resist entropy. You question what already exists before adding more process or tools. * Solve the category. You fix recurring problems at the source, not one case at a time. * Work at the frontier. You go after problems no one has solved yet. We look for people who already work this way, and these principles shape how we grow and evaluate our team. ### AI-First by Default We build AI that millions of people talk to, and we run the company the same way. AI is how we centralize work here, and is core to how we operate. That means more than using the tools. We expect you to: * Build, not just use. Design your own workflows and agents, and use AI to remove whole categories of work instead of doing the same work faster. * Own the output. AI can speed up the work. It doesn’t make anyone less accountable for it. You understand, verify, and stand behind everything you share, including what an agent produces for you. * Spend the time you get back at the frontier. The capacity AI creates goes toward problems no one has solved yet. * Keep pace. Models and tools change every month, and so will your work. You adopt what’s new quickly and drop what no longer earns its place. We judge AI by what it helps you deliver and whether you stand behind the result, not by how often you use it. In interviews, expect to walk us through a workflow you built and where you drew the line between AI and your own judgment. ### The Opportunity We’re building the engine that solves customer challenges at scale. When customers hit a limit in our voice AI, you’ll measure it, fix it, and ship the fix to every customer in weeks. Example projects: * Expanding our models to new languages and regions * Improving how text-to-speech pronounces names, terms, and numbers * Isolating a speaker’s voice from background noise and crosstalk * Giving customers early access to new voice capabilities, like steerable and cloned voices, and using their feedback to guide the research methodology * Releasing new benchmarks for Voice AI use-cases ### The Role You will thrive in this role if you enjoy: * Turning vague customer problems into clear metrics * Using whatever works to fix them, including fine-tuning, new data, and external models * Shipping work that customers use within weeks ### The Challenge We are seeking researchers who: * Own a customer problem until it measurably improves in production * Find the fastest path to a fix, even if it’s an approach no one has tried * Can tell when a result is ready for customers If these expectations energize you, you might be the person we need. This role takes focus on the customer’s problem, creativity in approach, and a strong drive to ship. ### What You’ll Do * Find where our models fall short and set targets for improvement * Design evaluations that show whether a model is better and why * Curate and generate data and fine-tune models * Build working systems quickly and ship them to production We don’t require a specific degree. Show us hard problems you’ve solved in past projects, shipped systems, or open source work. ### You May Be a Good Fit If You Have * Deep experience training, fine-tuning, and evaluating deep learning models * Strong Python and PyTorch skills * A track record of shipping ML to production * A habit of owning problems end to end and learning what you need along the way * Good judgment about which problems matter and when a result is ready to ship ### Strong Candidates May Also Have * Experience with speech-to-text, text-to-speech, or voice agents * Experience with distributed training * Published papers, open source contributions, or public benchmarks 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. Show more [Apply now >](https://jobicy.com/jobs/161400-research-staff-applied-voice-ai.md) * ![Upload CV](data:image/svg+xml;base64,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) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content. ## 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 high-seniority applied research role requiring deep learning expertise and the ability to convert ambiguous customer needs into measurable, production-ready model improvements. The expectation to ship impactful work within weeks creates substantial technical and delivery pressure. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$243,000US market range (per year)$210k–$300k0$330k AI insightThe employer discloses a yearly base-pay range of $216,000 to $270,000 USD, with a midpoint of $243,000. This is a credible range for a senior applied machine learning research role focused on production voice AI in the US market. Estimated market compensation for comparable roles is approximately $210,000 to $300,000 USD per year. ## Core skills Skills and capabilities most closely associated with this opportunity. [Deep Learning](https://jobicy.com/jobs?search_keywords=Deep%20Learning.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[PyTorch](https://jobicy.com/jobs?search_keywords=PyTorch.md)[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)[Voice AI](https://jobicy.com/jobs?search_keywords=Voice%20AI.md)[Model Fine-Tuning](https://jobicy.com/jobs?search_keywords=Model%20Fine-Tuning.md)[Model Evaluation](https://jobicy.com/jobs?search_keywords=Model%20Evaluation.md)[Dataset Curation](https://jobicy.com/jobs?search_keywords=Dataset%20Curation.md)[Production Machine Learning](https://jobicy.com/jobs?search_keywords=Production%20Machine%20Learning.md)[Distributed Training](https://jobicy.com/jobs?search_keywords=Distributed%20Training.md)[Benchmarking](https://jobicy.com/jobs?search_keywords=Benchmarking.md)[Experiment Design](https://jobicy.com/jobs?search_keywords=Experiment%20Design.md)[Applied Research](https://jobicy.com/jobs?search_keywords=Applied%20Research.md) Sample interview questionsHow would you turn a vague customer complaint about speech-recognition quality into a research and delivery plan?I would first collect representative failures and define a measurable target, such as word error rate for a specific accent, domain, or acoustic condition. I would build an evaluation slice, establish a baseline, investigate likely causes, and test the fastest viable interventions such as data augmentation, fine-tuning, decoding changes, or a targeted model component. I would then validate the improvement against regression suites and production-relevant metrics before rollout. Describe how you decide whether to fine-tune an existing model or pursue a different technical approach. I compare the expected impact, data availability, latency and cost constraints, engineering complexity, and time to customer value. Fine-tuning is appropriate when the failure mode is well represented in quality data and the existing architecture has sufficient capacity. If the issue is structural, poorly addressed by available data, or constrained by inference requirements, I would evaluate alternatives such as data pipelines, model routing, external models, or system-level changes. What makes an evaluation suite useful for production voice AI? A useful suite represents real customer use cases, has clear metrics, includes difficult slices, and protects against regressions across languages, speakers, noise conditions, and domains. It should be reproducible, fast enough to support iteration, and connected to operational outcomes rather than only aggregate offline scores. I would also maintain qualitative error analysis so metric movement can be explained and trusted. How have you approached shipping machine learning systems under aggressive timelines? I start with a narrow, measurable problem definition and prioritize the smallest experiment that can disprove or validate the key hypothesis. I use existing infrastructure where possible, automate evaluation early, and make trade-offs explicit across quality, latency, reliability, and maintainability. Before release, I define monitoring and rollback criteria so the team can learn safely from production usage. How would you incorporate customer feedback into a speech or voice-model research roadmap? I would group feedback into recurring failure patterns, quantify their frequency and business impact, and convert the highest-value patterns into evaluation sets and research targets. I would partner with customer-facing and product teams to verify that the benchmark reflects the actual workflow. After shipping, I would measure whether the targeted failures improve in real usage and use the results to refine priorities. Next step ## Apply now. Follow the employer’s application method and review Jobicy’s safety guidance before sharing personal information. Keep exploring ## Related remote jobs. * ![Particle41 logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2026/11/06a9d08339e9-221.webp) Particle41 Nov 11 New ### [Data Engineer (Tableau)](https://jobicy.com/jobs/161424-data-engineer-tableau.md) Data Engineer Particle41 is seeking a talented Data Engineer to join our team. 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