Biomaterials Scientist

Sava
London
2 months ago
Applications closed

Join to apply for theBiomaterials Scientistrole atSava

Company Information
All the health information we need is within us. Just below the skin. SAVA is redefining the way people interact with their health by developing the most advanced biosensing technology science has to offer, capable of accessing bodily information in a painless, real-time and affordable way.

Description
We are looking for a Senior/Scientist to join the Biomaterials Team, within the Biosensing and In Vivo Performance Department. The selection and optimisation of the materials used in our sensors is of utmost importance for device performance and longevityin vivo. The ideal candidate will bring experience in materials characterisation, and an in-depth understanding of how various properties will impact the material-tissue interface, and so the function of the biosensor as a whole.

Responsibilities

  • Work within the Biomaterials Team to develop and characterise the materials being utilised in all aspects of a minimally invasive biosensor platform.
  • Collaborate with the Chemistry, Process, and Manufacturing teams to implement material changes for optimisation of sensor performance, longevity and safety.
  • Develop new protocols to further the understanding of various aspects of sensor composition and performance.
  • Utilise analytical techniques to understand how material characteristics impact sensor manufacturing and device function.
  • Coordinate testing to determine the biocompatibility of novel candidate materials.
  • Generate, analyse and report data to the team and wider company, verbally and in writing.
  • Maintain detailed, up to date records of experiments and data analysis.
  • Research and source new equipment to support critical research areas.
  • Liaise with internal and external stakeholders to coordinate projects, managing deadlines and requirements in parallel to advance device development.

Past Experience

Essential:Masters degree plus > 2 years lab experience post-graduation in a relevant discipline (e.g., materials science, bioengineering, tissue engineering), working with materials development and characterisation.

Preferred:PhD in a relevant discipline (e.g., materials science, bioengineering, tissue engineering), working with materials development and characterisation. Industry experience post-PhD would be beneficial.

Requirements

  • Multidisciplinary background is advantageous, given the requirements of this role. The candidate will be expected to work across departments, interacting with scientists, engineers, Operations, external suppliers and regulatory bodies.
  • Comfortable being hands on in the lab, and can demonstrate a degree of independence and ownership of work.
  • Adaptability - this role requires the ability to learn new skills and techniques quickly, and to deal with the fast pace of a startup environment.
  • Materials characterisation and/or biomaterials experience, which may include: mechanical characterisation, rheology, material hydration studies, hydrogel processing and characterisation, analyte diffusivity, cell or tissue culture, cell/material interfacing, bioactive materials, enzyme stability and activity, protein conjugation and characterisation, colorimetric or fluorescence-based assays, microscopy, SEM/EDX.
  • Experimental design - organisational skills and attention to detail.
  • Presentation and communication of data to both technical and non-technical audiences.
  • Excellent interpersonal and communication skills.

Preferred

  • Experience with biological systems preferred (but not essential, as long as proficiency with the different techniques required can be demonstrated).
  • Experience with and knowledge of biosensors or medical devices with skin/surface interaction.
  • Experience with ISO 10993 or other appropriate regulatory standards and biocompatibility testing methods.
  • Experience with data analysis and visualisation software (e.g., python, R, MATLAB).

Sava Technologies Ltd is an equal opportunity employer. We consider candidates regardless of age, ancestry, colour, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, or military or veteran status.

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Other

Industries

IT Services and IT Consulting

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jobs

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Jobs Careers

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Jobs

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.