Mid Level Machine Learning Research Jobs
Mid level machine learning research jobs go to researchers ready to own experiments end to end, make modeling decisions with limited oversight, and guide junior teammates through implementation. Openings run across Technology & Software, Artificial Intelligence, and Electronics & Hardware, with Scale AI, Apple, and Nuro among the employers competing for researchers at this level now.
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Showing 5 of 30+ Mid Level Machine Learning Research jobs
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.
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.
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Find JobsMid Level Machine Learning Research Job Market
Who's Hiring
- Scale AI15
- Apple5
- Nuro2
- Microsoft2
- Plaid1
Top Industries Hiring
- Technology & Software14
- Artificial Intelligence12
- Electronics & Hardware7
- Transportation & Logistics2
- Automotive1
Mid Level Machine Learning Research Jobs: Frequently Asked Questions
How do I get a mid level machine learning research job?
Position yourself as someone who has moved beyond executing tasks to owning outcomes. Highlight projects where you defined the research direction, selected methods, and delivered results independently. Applications stand out when you can point to published work, reproducible experiments, or deployed models rather than coursework. Tailor your resume to show scope of ownership, not just the tools or frameworks you have used.
Which companies hire mid level machine learning researchs?
Companies hiring mid level machine learning researchs right now include Scale AI, Apple, and Nuro, based on current listings on Migrate Mate as of August 2026. Hiring at this level comes from a mix of technology companies building core research teams, industry labs pushing applied work, and startups scaling their model development function.
Are there remote mid level machine learning research jobs?
Yes, though availability varies by employer and specialization. About 4% of mid level machine learning research openings are remote or hybrid as of August 2026, reflecting how research workflows have adapted to distributed collaboration tools and asynchronous experimentation cycles. Roles tied to proprietary hardware or lab infrastructure tend to require on-site presence.
How do I move up to a mid level machine learning research role?
The path to mid level comes from accumulating independent ownership over your first few years. That means progressing from running experiments under supervision to designing them yourself, building a portfolio of measurable results, and demonstrating that your judgment can be trusted without close oversight. Contributing to peer review, co-authoring research, or leading a meaningful project signals the readiness that mid level roles require.
Which industries hire the most mid level machine learning researchs?
Mid Level machine learning research roles concentrate in Technology & Software, Artificial Intelligence, and Electronics & Hardware, based on current listings on Migrate Mate as of August 2026. These sectors drive hiring because they generate the large proprietary datasets and face the complex prediction problems that make investing in dedicated research capacity worthwhile at this experience level.