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Quantum Machine Learning Engineer Jobs in Napa, CA

Superhuman

Machine Learning Engineer

San Francisco, CA · On-site

$180 - $260/hr

The Opportunity We are building a platform for AI Agents to come together and solve arbitrarily complex tasks, leveraging Superhuman ubiquitous UI. As a Machine Learning Engineer on this team, you ...

Canals

Machine Learning Engineer

San Francisco, CA · Remote

We're looking for a senior-level Machine Learning Engineer who can move quickly while maintaining high quality, owning end-to-end ML pipelines while shaping product features that deliver real-world ...

Goodfire

Machine Learning Engineer

San Francisco, CA · On-site

$200K - $400K/yr

About the role We're looking for Machine Learning Engineers to help build our platform for training, evaluating, and deploying interpretable AI systems at scale. You'll play a central role in ...

Superhuman

Machine Learning Engineer

San Francisco, CA · On-site

$250K - $385K/yr

The Opportunity We are building a platform for AI Agents to come together and solve arbitrarily complex tasks, leveraging Superhuman ubiquitous UI. As a Machine Learning Engineer on this team, you ...

Relace

Machine Learning Engineer

San Francisco, CA · On-site

The Role We're looking for a Machine Learning Engineer who loves getting close to the metal. This is a hands-on engineering role focused on making models faster, more efficient, and more reliable ...

Sift

Machine Learning Engineer

San Francisco, CA · On-site

$160 - $230/hr

As a Machine Learning Engineer at Sift, you will bridge the gap between data science and large-scale distributed systems. You won't just train models in isolation; you will build end-to-end pipelines ...

Goodfire

Machine Learning Engineer

San Francisco, CA · On-site

$200K - $400K/yr

About the role We're looking for Machine Learning Engineers to help build our platform for training, evaluating, and deploying interpretable AI systems at scale. You'll play a central role in ...

Kikoff

Senior Machine Learning Engineer

San Francisco, CA · On-site

$123K - $169K/yr

We are seeking a Senior Machine Learning Engineer to join our team. This role will focus on developing and maintaining machine learning infrastructure and operations, particularly for our cash ...

Kikoff

Senior Machine Learning Engineer

San Francisco, CA · On-site

$123K - $169K/yr

We are seeking a Senior Machine Learning Engineer to join our team. This role will focus on developing and maintaining machine learning infrastructure and operations, particularly for our cash ...

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Showing results 21-40

All JobsQuantum Machine Learning Engineer JobsQuantum Machine Learning Engineer Jobs in Napa, CA

Quantum Machine Learning Engineer information

See Napa, CA salary details

$35.7K

$146K

$219.3K

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

As of Aug 11, 2026, the average yearly pay for quantum machine learning engineer in Napa, CA is $145,957.00, according to ZipRecruiter salary data. Most workers in this role earn between $115,000.00 and $175,700.00 per year, depending on experience, location, and employer.

What is a quantum machine learning engineer?

A Quantum Machine Learning Engineer is a professional who combines expertise in quantum computing and machine learning to develop algorithms and solutions that leverage quantum hardware for advanced data processing tasks. They work on designing, implementing, and testing quantum algorithms that can solve problems faster or more efficiently than classical computers. Their work often involves collaborating with physicists, data scientists, and software engineers to bridge the gap between quantum theory and practical applications. This role requires strong backgrounds in quantum mechanics, computer science, and statistical learning techniques.

How do quantum machine learning engineers typically collaborate with classical machine learning teams and quantum hardware specialists?

Quantum Machine Learning Engineers often serve as a bridge between classical machine learning experts and quantum hardware specialists. They work closely with data scientists to adapt machine learning algorithms for quantum environments and collaborate with hardware teams to ensure algorithms are optimized for specific quantum processors. Regular cross-functional meetings, code reviews, and joint problem-solving sessions are common, fostering a highly collaborative work environment. This collaboration is essential for successfully integrating quantum solutions into existing workflows and advancing the organization's quantum computing initiatives.

What are the key skills and qualifications needed to thrive as a quantum machine learning engineer?

To thrive as a Quantum Machine Learning Engineer, you need a strong background in quantum computing, machine learning, linear algebra, and programming (often Python or C++), typically supported by an advanced degree in physics, computer science, or a related field. Familiarity with platforms like Qiskit, Cirq, or TensorFlow Quantum, and knowledge of quantum algorithms and cloud-based quantum computing services are essential. Creative problem-solving, analytical thinking, and strong collaboration skills help distinguish top performers in this interdisciplinary field. Mastery of these skills enables innovation in developing and deploying quantum machine learning solutions to solve complex, cutting-edge problems.
What cities near Napa, CA are hiring for Quantum Machine Learning Engineer jobs? Cities near Napa, CA with the most Quantum Machine Learning Engineer job openings:
Quantum Machine Learning Engineer jobs near you

Machine Learning Engineer

Human Archive

San Francisco, CA • On-site

$100K - $150K/yr

Full-time

Re-posted 21 days ago

Job description

About Human Archive
Human Archive is a research lab focused on modeling human embodied intelligence.
Humans are the most sophisticated biological systems we have ever observed, yet we still do not fully understand ourselves. Research into human physical intelligence - including the human hand, proprioception, and vision - remains largely unsolved. Our mission is to recover human embodied intelligence as a learned model. To achieve this, we build custom hardware products, deploy them globally at scale, and publish research. Today, our data is used for robotics and world modeling, but the broader opportunity is advancing scientific research into intelligence itself.
Founded by Stanford and UC Berkeley researchers, we are lean, deeply technical, and operate at extreme speed, taking on unglamorous and conventionally impossible problems that directly unlock step-function gains in model capability.
The deployment of capable humanoids at scale will permanently redefine human labor. Undesirable physical work will disappear, and human effort will shift toward a new era of abundant creativity.
We are building the infrastructure to accelerate that transition by assembling the Human Archive mafia. You will own meaningful systems from day one and see your work directly impact model capabilities. This is a once-in-a-generation inflection point. If you want to help reshape physical labor and work on problems that matter at civilizational scale, join us.
The Opportunity
As a Machine Learning Engineer, you'll work on multimodal perception, VLA training, robotics post-training, and downstream policy evaluation. This is a hands-on role at the intersection of applied machine learning, data infrastructure, and robotics, where your work directly shapes how data is collected, validated, annotated, and evaluated.
You'll help close the loop between research and data collection by fine-tuning VLAs on downstream policy performance and building post-training and reinforcement learning systems around real-world robotics tasks. You'll be expected to make architectural decisions, own projects end-to-end, and operate in highly ambiguous research environments given the novelty and scale of our multimodal datasets.
Your work will help shape how frontier labs and leading robotics companies train their models, transforming physical labor markets and economies while contributing to broader research into human embodied intelligence.
What You'll Do
  • Build systems for multimodal perception, annotation, dataset QA, and robotics evaluation
  • Publish research on multimodal data by fine-tuning and evaluating VLA models on downstream robotics tasks and policy performance
  • Build post-training and reinforcement learning systems around robotics failure modes and corrective demonstrations
  • Work across video understanding, tracking, pose estimation, temporal modeling, and multimodal alignment
  • Develop tooling for benchmarking, observability, and temporal efficiency
  • Prototype quickly, ship rapidly, and iterate from real-world robotics deployments and research feedback
What We're Looking For
  • Passionate, mission-driven individuals who have demonstrated exceptional ownership in previous work
  • Engineers who want their work to directly impact the next frontier of physical AGI
  • Strong ML engineering fundamentals across robotics, computer vision, and perception systems
  • Experience with video understanding, tracking, pose estimation, robotics, or real-world sensor systems
  • Strong technical intuition and ability to move quickly in ambiguous research environments
  • Published research or production experience in robotics, embodied AI, reinforcement learning, motion capture, or vision systems is a strong plus

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