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Founding Machine Learning Engineer Jobs in Sunnyvale, CA

Advatix Inc.

Machine Learning Engineer

San Mateo, CA · On-site

$110 - $165/hr

  • Medical

  • Dental

  • Vision

  • PTO

Not Available Role Summary We are looking for a highly skilled Machine Learning Engineer / Research Engineer to join our founding team and help develop intelligent systems that transform how hardware ...

Known

AI / ML Engineer

San Francisco, CA · On-site

$200K - $375K/yr

Known - Founding Machine Learning Engineer * San Francisco, CA (In-Person) * 200k-375k Cash + Equity Known is a matchmaker that talks to users and supports them like a friend. Our mission is to ...

Winaxis

Machine Learning Engineer

Fremont, CA · On-site

Machine Learning Engineer Location: Fremont, CA (Local) Onsite interview Duration: 12+ Mos H1B Only h1 candidate About the Role: Our direct client is hiring a Machine Learning Engineer for their ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...

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All JobsFounding Machine Learning Engineer JobsFounding Machine Learning Engineer Jobs in Sunnyvale, CA

Founding Machine Learning Engineer information

See Sunnyvale, CA salary details

$37K

$151.1K

$227.1K

How much do founding machine learning engineer jobs pay per year?

As of Aug 16, 2026, the average yearly pay for founding machine learning engineer in Sunnyvale, CA is $151,130.00, according to ZipRecruiter salary data. Most workers in this role earn between $119,100.00 and $181,900.00 per year, depending on experience, location, and employer.

What is a founding machine learning engineer?

A Founding Machine Learning Engineer is one of the first technical team members at a startup who specializes in designing, building, and deploying machine learning systems. This role involves working closely with the founders to set the technical direction, build core AI products, and establish best practices for data and model development. In addition to hands-on coding and experimentation, a Founding Machine Learning Engineer often influences product decisions and helps shape the company's engineering culture. The role typically requires a blend of deep technical expertise, startup agility, and a willingness to tackle both high-level strategy and low-level engineering tasks.

What are some unique challenges and expectations for a founding machine learning engineer in an early-stage startup?

As a Founding Machine Learning Engineer, you'll face the unique challenge of building the company's machine learning infrastructure from the ground up, often with limited resources and rapidly evolving requirements. You'll be expected to wear many hats, from designing and deploying models to setting up data pipelines and collaborating closely with product and engineering teams. Your role will also involve making critical decisions about technology stacks and best practices that will shape the company's technical direction. Additionally, you'll have significant influence on the company's culture and have ample opportunities for growth as the team expands.

What are the key skills and qualifications needed to thrive as a founding machine learning engineer, and why are they important?

To thrive as a Founding Machine Learning Engineer, you need deep expertise in machine learning algorithms, software engineering, and data science, often supported by a degree in computer science or a related field. Familiarity with tools such as Python, TensorFlow or PyTorch, cloud platforms, and experience deploying ML models in production are typically required. Strong problem-solving abilities, entrepreneurial mindset, and excellent communication skills set standout candidates apart. These skills and qualities are vital for driving innovation, building scalable solutions from scratch, and collaborating within a fast-paced startup environment.

How much does a founding machine learning engineer make?

A founding machine learning engineer typically earns between $120,000 and $180,000 annually, depending on experience, location, and company size. Equity and bonuses are also common components of compensation, especially in startup environments where they play a significant role in total earnings.

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Founding Machine Learning Engineer jobs near you

Founding Machine Learning Engineer

NeuroTech X

San Francisco, CA • On-site

$180 - $280/hr

Other

Posted 4 days ago

Job description

Founding Machine Learning Engineer About the job

Who we are

Voyage is a Maison for Mind Computing – the first deep tech couture house, engineering non-invasive neural interfaces as objects of desire. With a founding team from Apple, MIT Media Lab, Harvard, the Royal College of Art and more, we blend frontier science with cultural gravity to create technology that is equally functional, aesthetically refined, and highly adoptable. All team members are known as ‘Technical Philosophers’ and collaborate at the intersection of hardware, AI/ML, neuro, design, and philosophy of mind. Applicants can expect a creative, experimental environment focused on pushing the boundaries of human-machine interaction.

Most ML roles begin with “which architecture fits this data.” We like to start with “what is this data, actually.”

There’s no established benchmark for this, no textbook preprocessing pipeline, no prior work to sanity-check yourself against. You’re not fitting a model to a known modality – you’re the first person deciding what the modality even is, both structurally (what’s actually sitting in the raw signal) and functionally (how those patterns turn into something usable). That’s a different kind of endeavour than most ML roles: less “optimize against a metric,” more “feel out a pattern nobody’s named yet, then prove it’s real.” Voyage poetics.

What we need
  • Strong deep learning fundamentals, with a feel for designing architectures around new inductive biases
  • Sharp sense for representation learning and working with unlabeled data
  • Ability to reason about signal-to-noise situations and integrate multiple modalities
  • Experimental rigour and speed under high uncertainty – creating and testing hypotheses quickly, with direct oversight of hardware and data collection
  • Experience building foundational models, designing tokenizers, and handling continual learning despite domain shift is highly useful
Who we’re looking for

We care more about people who developed neurotech interest from an ML fundamentals background than people who started in neurotech and backed into modeling.

On Voyage

ML with us means self-supervised approaches to calibrating across modalities that were never designed to talk to each other. You’ll have a direct line to the hardware and data collection loop – when the data looks wrong, you’re close enough to the sensor stack to find out why, not waiting on someone else to tell you. The playbook for building in Mind Computing has not yet been written – you will be one of the first to do so.

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