Quantum Machine Learning Engineer - Remote
📌United States, United States 🇺🇸
⏱︎ full-time
🧙♂️ mid-level
remote
Key Responsibilities
Design, implement, and optimize quantum machine learning (QML) algorithms for near-term and future quantum hardware.
Develop and test hybrid quantum-classical models using platforms such as PennyLane, Qiskit, Cirq, or TensorFlow Quantum.
Collaborate with data scientists, quantum researchers, and software engineers to integrate QML solutions into real-world applications.
Conduct benchmarking and performance analysis across quantum and classical platforms.
Stay up to date with the latest advancements in QML and publish findings or contribute to open-source projects when applicable.
Participate in code reviews, team meetings, and remote collaboration activities.
Qualifications
Required:
MSc or PhD in Physics, Computer Science, Mathematics, or a related field.
Strong foundation in quantum computing principles and quantum information theory.
Experience with machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
Proficiency in Python and experience with at least one quantum SDK (Qiskit, PennyLane, Cirq, etc.).
Strong problem-solving and analytical skills.
Preferred
Research or industry experience in quantum machine learning or variational quantum algorithms.
Experience with cloud-based quantum computing platforms (IBM Q Experience, AWS Braket, Xanadu, etc.).
Familiarity with software development best practices (Git, CI/CD, testing).
Demonstrated ability to work independently in a remote team environment.
Senior Embedded Software Engineer
@ Embedd 
What we're looking for
At embedd.it, we power hardware-software integration in embedded devices. We turn fragmented semiconductor chip data into configurable digital twins, then deterministically generate code, tests, and documentation – making integration faster, cheaper, and resilient to supply chain shocks.
We’re looking for a true legend – someone with abstract thinking and deep expertise in low-level software development. You’ll build vendor-agnostic tooling for MCU and peripheral integration to solve hardware-software integration once and for all. We work at the intersection of embedded systems, code generation, compilers, and machine learning. It’s your chance to help redefine how low-level development is done.
Responsibilities
- Drive the architecture of the driver structures and system layout
- Define scalable patterns for cross-architectural integration across bare-metal, Zephyr, Linux, and other environments
- Deconstruct complex SoC device trees and translate hardware descriptions into structured internal models for automated reasoning
- Partner with the ML team to shape data and annotation models for AI-driven data-extraction tooling
Qualifications
- 5+ years of experience in embedded systems development, including bare-metal and RTOS-based systems
- Proficiency in C/C++
- Familiarity with firmware configuration, SoC bring-up, and system-level debugging
- Hands-on experience with code generation tools, or low-level compiler/decompiler/emulator technologies
- Strong background in embedded toolchains: compilers, linkers, debuggers, and build systems
- Python or scripting for automation and tooling is a plus
- Experience with functional-safety standards is a plus
Why embedd.it?
- Dynamic, venture-backed team of engineers challenging the status quo 🚀
- Work at the intersection of embedded systems, code generators, compilers, and ML 🤖
- Unusual challenges requiring out-of-the-box abstract thinking 🧠
- Competitive compensation: cash + equity 💰
- Remote work possible 🌍
About Us
embedd.it is a UK-based startup reinventing embedded development. Our platform transforms unstructured semiconductor data into structured digital twins, enabling automatic generation of drivers, tests, and documentation. We're building a single, traceable source of truth to make integration fast, vendor-agnostic, and future-proof.
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-
Employment
⏱︎ full-time
-
Experience
🧙♂️ mid-level
-
Working model
remote
-
Skills
-
Industry
-
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