At a Glance
- Tasks: Develop cutting-edge ML models for wearable health tech, optimising sensing pipelines.
- Company: Innovative health tech company focused on wearable technology.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Join an early-stage company with significant growth potential and technical influence.
- Why this job: Make a real impact in health tech while tackling unique challenges.
- Qualifications: Strong background in signal processing and applied ML; Python proficiency required.
The predicted salary is between 60000 - 80000 £ per year.
A health technology company is seeking an On-Device ML Engineer to develop machine learning models that run directly on wearable devices, extracting reliable health signals under strict real-world constraints. This is a technically deep and highly impactful role, sitting at the intersection of signal processing, applied ML, and embedded systems. You’ll work closely with hardware and firmware teams to optimise end‑to‑end sensing pipelines, tackling problems that very few teams in the world are working on. You’ll have significant ownership over algorithm development from signal cleaning through to prototype integration.
In this position, you’ll develop physiological inference algorithms for wearable health products, build methods to extract reliable cardiovascular and autonomic health metrics from real-world data, and advance hybrid DSP + ML approaches for continuous health sensing — all within tight compute and power budgets.
What they’re looking for:
- Strong background in signal processing and applied machine learning
- Experience deploying ML models on embedded or edge devices
- Proficiency in Python; C/C++ experience is a plus
- Understanding of physiological signals and noisy, real-world sensor data
- Ability to balance accuracy, efficiency, and robustness under hardware constraints
Why consider it:
- Work on frontier problems in medical‑grade wearable inference
- High ownership across the full algorithm pipeline, from research to integration
- Close collaboration across ML, hardware, and firmware disciplines
- Early‑stage company with significant growth potential and technical influence
On-Device ML Engineer for Wearable Health Tech employer: IC Resources
Join a pioneering health technology company as an On-Device ML Engineer, where you'll have the opportunity to work on cutting-edge wearable health tech that makes a real difference in people's lives. With a strong emphasis on collaboration and innovation, you'll enjoy a dynamic work culture that fosters professional growth and encourages you to take ownership of impactful projects. Located in a vibrant tech hub, this early-stage company offers significant potential for career advancement and the chance to be at the forefront of medical-grade inference technology.
StudySmarter Expert Advice🤫
We think this is how you could land On-Device ML Engineer for Wearable Health Tech
✨Tip Number 1
Network like a pro! Reach out to folks in the health tech and ML space on LinkedIn or at meetups. We can’t stress enough how personal connections can open doors that applications alone can’t.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to signal processing and ML. We love seeing real-world applications of your work, especially if it’s relevant to wearable tech.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and C/C++ skills. We recommend doing mock interviews with friends or using online platforms to get comfortable with the types of questions you might face.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always on the lookout for passionate candidates who want to make an impact in health tech.
We think you need these skills to ace On-Device ML Engineer for Wearable Health Tech
Some tips for your application 🫡
Show Your Passion for Health Tech:When writing your application, let us see your enthusiasm for wearable health technology. Share any relevant projects or experiences that highlight your interest in this field and how you can contribute to our mission.
Highlight Your Technical Skills:Make sure to showcase your strong background in signal processing and applied machine learning. Be specific about your experience with deploying ML models on embedded devices and your proficiency in Python, as these are key to the role.
Tailor Your Application:Don’t just send a generic application! Tailor your CV and cover letter to reflect the job description. Mention how your skills align with the responsibilities of developing physiological inference algorithms and working with hardware teams.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity in our growing team!
How to prepare for a job interview at IC Resources
✨Know Your Signal Processing
Brush up on your signal processing fundamentals. Be ready to discuss how you’ve applied these concepts in real-world scenarios, especially in relation to wearable tech. This will show that you understand the core of what they’re looking for.
✨Showcase Your ML Deployment Experience
Prepare examples of your experience deploying machine learning models on embedded or edge devices. Highlight any challenges you faced and how you overcame them, as this will demonstrate your problem-solving skills and technical depth.
✨Familiarise Yourself with Physiological Signals
Make sure you understand the physiological signals relevant to health metrics. Being able to discuss how you would extract reliable cardiovascular and autonomic health metrics from noisy data will set you apart from other candidates.
✨Collaborate and Communicate
Since this role involves working closely with hardware and firmware teams, be prepared to discuss your collaboration experiences. Share how you’ve effectively communicated complex technical ideas to non-technical team members, as this is crucial in a cross-disciplinary environment.
