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Founding Machine Learning Engineer Jobs in Sunnyvale, CA

Nace AI

Machine Learning Engineer

Palo Alto, CA · On-site

As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine learning research into scalable, production-ready solutions. You will collaborate closely with cross ...

PROPRIUS

Machine Learning Engineer

San Francisco, CA · On-site

Machine Learning Engineer Location: San Francisco, CA Sponsorship: No Relocation: No Industry: Machine Learning Join an artificial intelligence company in San Francisco that excels at visual ...

Qubeaxis

Machine Learning Engineer

San Francisco, CA · On-site

$130 - $180/hr

Job Title Machine Learning Engineer Job ID 20985 Location Work Mode Onsite About the Team Our ML Platform team builds intelligent systems that power recommendations, forecasting, ranking ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...

Apple

Machine Learning Engineer

Santa Clara, CA · On-site

Our team comprises a diverse range of backgrounds, including applied machine learning engineers with a focus on ML and LLM, and experienced distributed systems engineers. As such, we are seeking ...

Poesis

Machine Learning Engineer

San Francisco, CA · On-site

$200K - $280K/yr

We're looking for an exceptional Machine Learning Engineer to help build the systems that make this possible. In this role, you'll develop models, signals and evaluation frameworks that power ...

Lyft

Machine Learning Engineer

San Francisco, CA · On-site

Our highly motivated Machine Learning Engineers work on these challenging problems and define solutions to directly impact various aspects of our core business. The Fulfillment group, within the ...

New

Lyft

Machine Learning Engineer

San Francisco, CA · On-site

Our highly motivated Machine Learning Engineers work on these challenging problems and define solutions to directly impact various aspects of our core business. The Fulfillment group, within the ...

New

NR Consulting

Machine Learning Engineer

Fremont, CA · On-site

NR Consulting is a company focused on innovative technology solutions, and they are seeking a Machine Learning Engineer to develop and deploy lightweight machine learning models for edge AI ...

Latent

Machine Learning Engineer

San Francisco, CA · On-site

$225K - $300K/yr

Machine Learning Engineer About Latent Health Healthcare today is only truly personalized for two groups: those with wealth and access, and those with physicians in their immediate family. For ...

Skild AI

Machine Learning Engineer

San Mateo, CA

Position Overview We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and ...

PhysicsX

Machine Learning Engineer

San Francisco, CA · On-site

Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple ...

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Showing results 21-40

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 Sep 7, 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.

Are founding machine learning engineers still in demand?

Founding machine learning engineers remain in high demand as companies seek to develop AI-driven products and services. They often require strong skills in deep learning, data modeling, and proficiency with tools like TensorFlow or PyTorch, with demand driven by growth in AI applications across industries.

How much does a founding machine learning engineer make?

A founding machine learning engineer typically earns between $100,000 and $180,000 annually, depending on experience, location, and company size. Equity and bonuses may also be part of the compensation package, especially in startup environments where they play a significant role in total earnings.

What are popular job titles related to Founding Machine Learning Engineer jobs in Sunnyvale, CA?

For Founding Machine Learning Engineer jobs in Sunnyvale, CA, the most frequently searched job titles are:

What job categories do people searching Founding Machine Learning Engineer jobs in Sunnyvale, CA look for?

The top searched job categories for Founding Machine Learning Engineer jobs in Sunnyvale, CA are:

What cities near Sunnyvale, CA are hiring for Founding Machine Learning Engineer jobs?

Cities near Sunnyvale, CA with the most Founding Machine Learning Engineer job openings:

Founding Machine Learning Engineer jobs near you

Machine Learning Engineer

Nace AI

Palo Alto, CA • On-site

Full-time

Re-posted 20 days ago

Job description

Role Overview:
As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine learning research into scalable, production-ready solutions. You will collaborate closely with cross-functional teams to identify opportunities where ML can drive product value, architect robust model-centric systems, and ensure their seamless integration into real-world applications. The role requires a strong balance between theoretical understanding and engineering execution, with a focus on building reliable, maintainable, and high-impact AI-driven features that align with Nace.AI's strategic objectives.
Key Responsibilities:
  • Design, build, and maintain end-to-end ML systems, including synthetic data pipelines, model training, debugging, and performance evaluation.
  • Fine-tune large language models (LLMs) and implement meta-learning methods to enhance model generalization and efficiency.
  • Improve existing Nace.AI models by incorporating advancements from recent ML research.

Qualifications:
  • 3+ years of hands-on experience building and deploying machine learning systems in production environments.
  • Hands-on experience training and fine-tuning large language models (LLMs) and vision-language models (VLMs), including practical work with pre-training, instruction tuning, and alignment techniques (GRPO,RLHF/DPO/PPO).
  • Hands-on Experience with Deep Learning Models, especially Transformers.
  • Ability to translate cutting-edge research from papers into clean, production-ready code (Paper to Code).
  • Proven experience scaling inference infrastructure for LLMs/VLMs, including expertise in model serving frameworks like vLLM, TGI.
  • Proficient in Python with a strong track record of building substantial projects.
  • Solid foundation in computer science fundamentals (data structures, algorithms, design patterns).
  • BS degree in CS or related technical field.
  • Solid Experience with ML frameworks and libraries (PyTorch, TensorFlow).
  • Self-starter comfortable working in a fast-paced, dynamic environment.

Preferred Qualifications:
  • 5+ years of industry experience in machine learning engineering, with a track record of shipping LLM-based systems at scale.
  • MS/PhD in CS or related technical field.
  • Familiarity with data processing stacks such as Spark and Airflow.
  • Experience with multi-node GPU training.
  • Contributor to open-source ML projects.
  • Deep knowledge in Linear Programming.
  • Experience with advanced NLP and Multimodal post-training experience (e.g., model distillation, quantization, deployment optimization).
  • Experienced in inference time optimization, deep understanding of LLM serving optimizations for LLMs/VLMs.
  • Hands on experience with quantization techniques (AWQ, GPTQ, FP8/GGUF).

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