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Phd Machine Learning Startup Jobs (NOW HIRING)

Ayo Semiconductor

Machine Learning Architect

Boston, MA · On-site

About Ayo We are a venture-backed early-stage startup developing processors specialized for machine ... PhD in machine learning, representation learning, theory of computation, or a related field - or ...

Cartesian

Machine Learning Scientist

Cambridge, MA · On-site

Required : • PhD in computer science, electrical engineering, or related field. • Deep ... Preferred : • Past startup experience • Industry experience in applied software or ML ...

Pattern AI

Machine Learning Engineer

San Mateo, CA

Company Description PatternAI is an automated machine learning platform that reveals critical ... Additional Information About PatternAI PatternAI is an early stage startup that is growing rapidly ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

Company Description PatternAI is an automated machine learning platform that reveals critical ... Additional Information About PatternAI PatternAI is an early stage startup that is growing rapidly ...

Point72

Machine Learning Engineer

New York, NY · On-site

PhD or PhD candidate in machine learning, computer science or other AI related research fields * Experience with sequential modeling and time series forecasting using deep learning * Experience with ...

Spotter

Machine Learning Scientist

Culver City, CA · Remote

You thrive in a fast-paced startup environment and are motivated by building models that don't just ... Who You Are * Master's degree or PhD in Computer Science, Statistics, Applied Mathematics ...

Spotter

Machine Learning Scientist

Culver City, CA · On-site

You thrive in a fast-paced startup environment and are motivated by building models that don't just ... Who You Are * Master's degree or PhD in Computer Science, Statistics, Applied Mathematics ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

As part of our machine learning team, you will play a vital role in prototyping foundational ... Preferred Qualifications MS/PhD in computer vision, electrical, optical or computer engineering or ...

Docugami

Machine Learning / Data Scientist

Kirkland, WA · On-site

Docugami is looking for Machine Learning, Data Science and Math PhD researchers to work alongside ... You enjoy working at the very cutting edge of R&D * You want to experience an early stage startup ...

Eccalon LLC

Machine Learning Engineer

Detroit, MI

MS. or PhD in Machine Learning, or related field * Extensive AWS or GCP experience putting scalable Machine Learning systems into production. * Experience working with extremely high volume / high ...

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Phd Machine Learning Startup information

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How much do phd machine learning startup jobs pay per year?

As of Jun 28, 2026, the average yearly pay for phd machine learning startup in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced by PhD-level professionals working in machine learning startups?

PhD-level professionals in machine learning startups often encounter challenges such as balancing research innovation with the need for rapid product development. Unlike academia, startups prioritize practical solutions that fit tight deadlines and resource constraints. Team members typically wear multiple hats and collaborate closely with engineers, product managers, and business stakeholders, requiring strong communication skills and adaptability. Additionally, translating cutting-edge research into scalable, real-world applications can be both intellectually rewarding and demanding.

What do PhD holders in Machine Learning do at startups?

PhD holders in Machine Learning at startups typically lead research and development efforts to create innovative algorithms and models that solve real-world problems. They often work on designing and implementing advanced machine learning solutions, analyzing large datasets, and collaborating with product and engineering teams to bring research ideas to production. Their expertise helps startups stay competitive by driving technological advancements and fostering a culture of innovation.

What are the key skills and qualifications needed to thrive as a PhD-level Machine Learning professional in a startup environment, and why are they important?

To excel as a PhD-level Machine Learning professional at a startup, you need advanced expertise in machine learning algorithms, statistical modeling, and a doctoral degree in a related field. Experience with Python, TensorFlow, PyTorch, and version control systems, along with a strong publication record, is typically expected. Initiative, adaptability, and excellent problem-solving and communication abilities are crucial soft skills in the fast-paced startup setting. These competencies enable rapid innovation, effective team collaboration, and successful deployment of machine learning solutions under resource constraints.
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What cities are hiring for Phd Machine Learning Startup jobs? Cities with the most Phd Machine Learning Startup job openings:
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Phd Machine Learning Startup jobs near you
Infographic showing various Phd Machine Learning Startup job openings in the United States as of June 2026, with employment types broken down into 76% Full Time, 23% Part Time, and 1% Temporary. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Machine Learning Architect

Ayo Semiconductor

Boston, MA • On-site

Full-time

Medical, Dental, Vision

Posted 11 days ago

Job description

About Ayo

We are a venture-backed early-stage startup developing processors specialized for machine learning. The processor will provide orders or magnitude improvement in speed and power efficiency with a goal of unseating the GPU as the dominant computing platform for AI.

The Role

We are seeking a deep ML practitioner to join as a founding team member - someone with hands-on experience working on or alongside a foundation model at scale, who understands what happens under the hood when splitting jobs across thousands of GPUs, and who is excited to bring that depth to novel hardware. They have a full-stack understanding of machine learning architectures, love to optimize algorithms across disciplinary boundaries, and will deploy and train models directly on our prototype chips to help us prove out what our processor can do - no prior hardware experience required.

What You’ll Do:

  • Deploy and run trained models on prototype hardware and digital twins, producing working demonstrations on our chips.

  • Develop and adapt algorithms to train models on novel processing environments, including our prototype hardware.

  • Work with hardware engineers to define and refine processor architecture based on insights learned through model training and experimentation.

  • Maintain a deep curiosity about what makes machine learning systems work - and bring that curiosity to bear on how they run on new hardware.

  • Support go-to-market strategy development

What We’re Looking For:

  • PhD in machine learning, representation learning, theory of computation, or a related field - or equivalent industry experience working on foundation models at scale.

  • Experience training models at scale - distributed training across many GPUs, working with large datasets and compute.

  • Has built and trained neural networks from scratch

  • Deep knowledge of the structure and internal operation of neural networks - including how and why they behave the way they do (e.g. interpretability or explainability work is a plus).

  • Excitement about applying deep AI expertise to new and novel hardware environments - you don’t need prior experience with photonics or silicon, but you want to learn.

  • Fluent knowledge of Python

  • Fluency in PyTorch (preferred), TensorFlow, JAX, or other industry-standard ML software libraries

Why Ayo

  • You'll be a founding AI team member - shaping how we think about and deploy AI as a company.

  • We are working on a genuinely hard and interesting problem: unseating the GPU as the dominant AI compute platform.

  • Early-stage means real ownership, real impact, and meaningful equity.

  • Competitive salary. Equity commensurate with stage and seniority. Benefits package including health, dental, and vision.

  • Fully onsite in Boston - we are a collaborative, in-person team.