Google Rf Engineer Jobs in Boston, MA (NOW HIRING)

You will contribute to a team responsible for:
• Setting the overall strategy and vision for the machine learning and data science initiatives in the company. Provide strategic guidance and mentorship to the data science and engineering teams.
• Hiring, training, and developing team members to foster a culture of innovation and excellence.
• Collaborating with other department heads, such as product management, engineering, and healthcare experts, to align machine learning initiatives with company-wide goals.
• Building strong relationships with stakeholders and effectively communicating the value and impact of the team's work.
• Managing the budget and resources for the machine learning and data science function. Making strategic decisions on investments in tools, technologies, and talent to support the team's objectives.
• Staying at the forefront of advancements in machine learning and healthcare AI, and actively contributing to the broader scientific community through publications, conferences, or industry events.
• Representing the company as a thought leader and building strategic partnerships with academic institutions, research organizations, or industry partners.
• Developing and optimizing complex machine learning models, such as CNNs, LSTMs, and transformers, to tackle challenging problems and enhance system performance and scalability.
• Designing and implementing machine learning models for data analysis, feature extraction, and classification tasks.
• Ensuring real-time performance and resource efficiency by implementing and validating algorithms on embedded systems.
• Working closely with hardware engineers to optimize machine learning models for specific hardware architectures and assisting in system integration.
• Conducting performance evaluations and simulations to validate model performance and identify areas for improvement.
• Collaborating with software engineers to integrate machine learning models into software frameworks.
• Clearly communicating technical concepts and progress not only within your team but also to other teams across the company. Ensuring that all departments understand the work of the machine learning team and how it aligns with and supports the broader company goals.
Key Technical Requirements
To hit the ground running, you are comfortable across the entire engineering life cycle from system design to model development to model training to system testing and optimization, leading to a successful product launch and ongoing improvements. In particular:
Proven industry experience as an AI/ML Engineer, with a focus on radar and RF signals, video and image processing, or classification.
Proficiency in implementing signal processing algorithms using programming languages such as Python, MATLAB, or C/C++
Fluency in Python, machine learning frameworks (Pytorch, Tensorflow, etc.), data science tools (NumPy/Scikit-learn), and cloud services (Google Cloud Platform).
Extensive practical knowledge of Machine Learning techniques for images, deep learning network architectures (CNNs, GANs, etc.), regularization, loss functions, optimization strategies, etc.
Experience with building and orchestrating multiple models using parallel and sequential architectures, tailoring the approach to the specific requirements and constraints of the problem at hand.
Solid understanding of software development methodologies and version control systems.
Experience with real-time signal processing, DSP platforms, or embedded systems.
Strong knowledge of digital signal processing theory and techniques, including filtering, modulation, demodulation, and spectral analysis.
In addition, while not essential, it is a definite plus if:
You have worked with C/C++ for embedded systems
You have proficiency in radar signal processing techniques, DSP algorithms, and the entire ML lifecycle
Familiarity with radar principles, systems, and waveforms.
You have experience with one or more of the following: radar-to-image projection, image segmentation, video object detection, target detection in radar signal.
You have expertise in radar signal processing and object detection from RF signals, including algorithms such as CFAR, SVD, and other radar signal processing techniques.
You have experience implementing and optimizing data augmentation techniques (e.g., image flipping, rotation, scaling, filtering, text synonym replacement, SMOTE) to enhance model performance and robustness.
Personal Qualities
We'd love you to be self-aware, thoughtful, empathetic, diligent, hard-working, a lifelong learner, and a great team player. You'd show us that you have:
Strong interpersonal skills and the ability, perhaps even a passion, to build camaraderie and work effectively on difficult goals with a broad range of business and technical collaborators across cultures and skills.
Self-awareness to know your own superpower (nobody is great at all things) and the humility to permit others to exercise theirs on a team of accomplished specialists
Respect and empathy to recognize and support the goals of the company, your team, and colleagues in ways that build trust for people to feel safe to"disagree and commit" (The Amazon Way)
A work ethic that doesn't quit, that recognizes that time-to-market is often the only thing that separates teams that win from those that don't ("No matter how hard you work, someone else is working harder." - Elon Musk)
Tenacity and a dogged determination to never give up
Innate hunger to constantly do better and evolve both your work product and yourself (You're a lifelong learner)
The courage to move fast, break things, and ship products that people use ("Real artists ship" - Steve Jobs)
Exceptional communication skills characterized by meticulous attention to detail and precision in verbal and written expression. Consistently deliver clear, comprehensive, and precise information to ensure all team members are aligned and well-informed.
Education and Experience
A Master's or PhD degree in Artificial Intelligence, Mathematics, Statistics, Physics, Electrical Engineering, Computer Engineering, or a related field, or equivalent work experience.
10+ years of relevant experience in machine learning and data science, with healthcare AI a plus.
Proven track record of leading and scaling high-performing data science and machine learning teams and successfully delivering complex, impactful projects.
Real-world work experience in relevant roles in a commercial setting.
Ideally, full life cycle experience with a consumer or patient product that has shipped and achieved meaningful commercial success.
Preference for candidates with experience in a startup or fast-paced environment, as well as exposure to ambient sensing and sensor fusion technologies.