About dot.
dot. is a connected sensing platform business, combining AI with the science of olfaction to predict and prevent the spread of pests and disease. We build connected devices that use VOC sensor arrays and machine learning to detect biological targets in the real world.
We are a team of 44 people, with an active commercial pipeline spanning pest control, agriculture, animal health, conservation, and human health. We operate across the full stack, from sensor hardware and firmware through to cloud infrastructure, ML, and commercial deployment.
About the Role
We take a sensor-agnostic approach, building our products around commercially available components rather than tying ourselves to any single technology or supplier. Our advantage comes from how well we select, drive, sample, and interpret the parts already on the market, and from the data and models we build on top of them.
This is a hands-on role, embedded in the engineering team and working alongside our mechanical, electrical, and firmware engineers. You will own how commercial sensors perform inside our devices: which parts we choose, how they are tested and operated, how they behave once they leave the bench, and what we can reliably claim about the signal they produce.
You will work closely with the R&D and data science functions, who design the experiments and build the detection models, and with hardware and firmware on integration, drive electronics, and sampling. Your work is the connection between the datasheet, the device, and the performance a customer actually experiences.
Key Responsibilities
Sensor selection and evaluation
- Evaluate and benchmark commercially available sensors against our detection requirements, and make evidence-based recommendations on what we should be designing in.
- Test candidate parts against real targets and real conditions rather than relying on manufacturer specifications.
- Assess parts on the practical criteria that decide whether a product ships: unit cost, availability, lead time, lifetime, batch consistency, and supplier roadmap.
- Manage supplier relationships and stay close to what is coming to market.
Characterisation and optimisation
- Characterise sensor performance in our devices: sensitivity, selectivity, response and recovery, repeatability, and long-term stability.
- Optimise operating conditions - heater profiles, duty cycles, sampling regimes, and reference cycles - to improve signal quality and manage drift.
- Investigate and resolve device-to-device variability, and define the calibration and compensation approaches that address it in production.
- Design and run bench testing covering interferents, humidity and temperature effects, ageing, and the other conditions our devices will meet in the field.
Integration and productionisation
- Work with hardware and firmware engineers on sensor integration: placement, sampling architecture, drive electronics, and the trade-offs each involves.
- Translate sensing requirements into firmware and hardware specifications the engineering team can build against.
- Define what needs to be tested and calibrated at manufacture, and how.
- Diagnose sensing problems in fielded devices and feed the findings back into design.
Working with R&D and data science
- Support experimental design with a clear view of what the sensors can and cannot resolve.
- Produce characterisation data in a structured, documented form the data science team can use directly.
- Help diagnose detection problems where the cause could sit in the sensor, the device, the experiment, or the model.
- Contribute sensing expertise to grant-funded and collaborative R&D projects.
What You Bring
Must-haves
- Hands-on experience working with commercially available gas or chemical sensors in a product setting, including integrating them into devices that were built and deployed in the field.
- Hands-on experience characterising and optimising sensor performance in a product development setting.
- Working knowledge across more than one gas sensing technology - for example MOX, electrochemical, PID, NDIR, or acoustic - with real depth in at least one. We do not expect extensive hands-on experience with all of them.
- A practical understanding of what goes wrong with gas sensing outside the lab: drift, humidity and temperature effects, cross-sensitivity, poisoning, ageing, and part-to-part variation.
- Comfort with data analysis and the tools that go with it, whether Python, MATLAB, or equivalent.
- A degree in chemistry, physics, materials science, electronic engineering, or a related discipline; a PhD in a relevant field is welcome.
- The ability to work independently within an engineering team, set your own priorities, and take a problem from question to answer without close supervision.
Desirable
- VOC sensing, olfactory science, or biological target detection.
- Sampling system design: pumps, valves, flow paths, and enclosures.
- Experience supporting a product through manufacture, including production calibration and test.
- Exposure to machine learning applied to sensor data, enough to work productively with the data science team.
What Success Looks Like
Within twelve months:
- Sensor selection decisions are made on our own test data rather than on datasheets.
- We can predict how our devices will perform in a new environment instead of finding out after deployment.
- Device-to-device variability is a known, quantified, and managed quantity, addressed in design and production rather than in the field.
- The engineering, R&D, and data science teams have a single trusted source of truth on what the sensing layer can deliver.
Pay: £60,000.00-£65,000.00 per year
Application question(s):
- This role is based in Dagenham, London, with a minimum of three days per week on site. Are you able to commit to this? (Yes/No)
- Have you worked on integrating commercially available (off-the-shelf) gas or chemical sensors into a product that was manufactured and deployed to customers? (Yes / No)
- Which gas sensing technologies have you worked with directly? (For example MOX / metal oxide, Electrochemical, PID, NDIR, Acoustic (SAW/FBAR/QCM), Optical, Other)
- Briefly describe a sensor you selected or specified into a product, and one thing about its real-world performance that differed from what the datasheet suggested
Experience:
- gas or chemical sensors hands on: 4 years (preferred)
Work authorisation:
- United Kingdom (required)
Work Location: Hybrid remote in Dagenham RM10 7FN
