[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, Post-Training AI Summary Deepgram is seeking a Research Engineer to improve production voice AI systems through post-training, evaluation, data development, and rapid deployment. The role centers on converting ambiguous customer issues into measurable model-quality targets and delivering solutions within weeks. Candidates need strong hands-on deep learning expertise, particularly Python and PyTorch, plus a record of bringing machine learning systems into production. Speech AI, distributed training, public research, and open-source contributions are valuable additional qualifications. This is a customer-driven research position with substantial end-to-end ownership and an emphasis on practical impact.[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 270k–338k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished11 Nov 2026Apply before11 Dec 2026Listing views30Application actions0Application 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/161406-research-engineer-post-training.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 senior, frontier-focused ML role requiring deep technical judgment across model training, evaluation, data curation, and production delivery. Success depends on independently solving poorly defined customer problems quickly while maintaining rigorous quality standards. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$304,000US market range (per year)$250k–$380k0$418.00k AI insightThe employer discloses a base salary range of $270,000-$338,000 USD per year, with a midpoint of $304,000. This is a credible annual range for a highly experienced US-based research engineer specializing in production deep learning and speech AI. The estimated US market range for comparable senior research engineering roles is $250,000-$380,000 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)[Model Fine-Tuning](https://jobicy.com/jobs?search_keywords=Model%20Fine-Tuning.md)[Model Evaluation](https://jobicy.com/jobs?search_keywords=Model%20Evaluation.md)[Machine Learning Productionization](https://jobicy.com/jobs?search_keywords=Machine%20Learning%20Productionization.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)[Data Curation](https://jobicy.com/jobs?search_keywords=Data%20Curation.md)[Benchmarking](https://jobicy.com/jobs?search_keywords=Benchmarking.md)[Distributed Training](https://jobicy.com/jobs?search_keywords=Distributed%20Training.md)[Experiment Design](https://jobicy.com/jobs?search_keywords=Experiment%20Design.md)[Customer Problem Solving](https://jobicy.com/jobs?search_keywords=Customer%20Problem%20Solving.md) Sample interview questionsHow would you turn a vague customer report about poor speech-model performance into a research plan?I would first collect representative examples and clarify the customer impact, then define a measurable failure mode and baseline metric. I would segment the issue by factors such as language, accent, noise condition, domain, or audio quality, build an evaluation set, and compare potential interventions. I would prioritize the fastest approach that can produce a trustworthy production improvement, then monitor the relevant metric after release. Describe your approach to deciding whether to fine-tune a model, collect more data, or use an external model. I would compare the expected quality gain, latency, cost, operational complexity, and time to deployment for each option. Fine-tuning is appropriate when the failure pattern is stable and enough representative data is available; data collection is necessary when the evaluation set reveals coverage gaps. An external model can be effective when it improves the customer experience quickly and meets reliability, privacy, and cost requirements. What makes a model evaluation useful for production decisions? A useful evaluation is representative of the target users and failure modes, has clear success thresholds, and can be reproduced over time. It should include aggregate metrics as well as slices that expose regressions for important customer segments. I would also pair offline results with production monitoring, because real traffic may differ from curated benchmarks. Tell us about a machine learning system you shipped to production. A strong answer should explain the original user or business problem, the data and model choices, the offline evaluation methodology, and the deployment architecture. It should include how performance, latency, reliability, and regressions were monitored after launch. The best examples also describe trade-offs made and measurable outcomes achieved for users. How do you balance rapid experimentation with responsible engineering quality? I separate fast exploration from release readiness. Early experiments should be lightweight and focused on reducing uncertainty, while production candidates need reproducible training, robust evaluation, observability, rollback plans, and clear ownership. This approach preserves speed without shipping results that cannot be understood, supported, or trusted. 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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