Principal Machine Learning Engineer

Time commitment: Full-time.          

Location: Hybrid is preferred, working 3 days in the London office (Monday, Wednesdays and Thursdays) and 2 days at home per week.  We consider alternative requests if this is not possible for you.

Reporting to: VP of Technology, Avishai Gurt

About Paloma

Our mission is to make NHS autism and ADHD assessments and care accessible within 4 weeks of a GP referral, and, in turn, help every child achieve their potential.

In 2024, our founders, Mark and Darshak, were shocked to find that children needing an NHS autism or ADHD assessment were waiting 4-10 years for these assessments. Delayed assessments can result in children being more likely to develop mental health conditions and fall behind in education. Furthermore, the much-needed pre/post-assessment support (outside of ADHD medications) is typically not funded by the NHS. 

These long waits have built up because of increased recognition and understanding of neurodevelopmental conditions, and a lack of investment and innovation within the NHS to meet growing demand.

Mark and Darshak, with their 25 years of combined NHS experience, including building services that have treated over 1,000,000 patients in obesity & ophthalmology, founded Paloma to change this. 

As of May 2026, we have successfully reduced waits to less than 3 months by;

• Taking a product-first approach to redesign the care pathway to give families a more consumer-like experience of care
• Our in-house product, engineering and AI team building our own autism and ADHD specific electronic health record and AI clinical support tool to free clinicians from their significant documentation burden to refocus on supporting families
• Investing in expanding the clinician workforce and upskilling them in new ways of working.

We are proud that Paloma is:

• Loved by families, with 4.9 out of 5 Trustpilot and Google ratings
• Care Quality Commission (CQC) registered and quality-focused
• Clinically-led, with a team of over 170 people, including over 100 clinicians supporting families through their care journey. We are hiring 100+ more roles over the next 12 months.
• A community of passionate, mission-driven and innovative team members who challenge the status quo and test and learn from new ideas and approaches.
• In control of our own destiny, as we are profitable and backed by leading healthcare investors Triple Point and Heal Capital

What’s next?

• Whilst we have reduced wait times, it is estimated that up to 500,000 children are still waiting for an NHS autism or ADHD assessment. We won’t rest until this comes down.
• We want to build new services to support families and children diagnosed with autism or ADHD who feel lost after their assessments when the NHS is no longer there for them.

The Opportunity

Autism and ADHD assessment has no laboratory test and no external ground truth. What separates a good assessment from an adequate one lives in the judgement of experienced clinicians, applied case by case. Making that judgement explicit — specific enough to build against, and to prove that a system is meeting it — is the central technical problem at Paloma.

We are looking for a Principal Machine Learning Engineer to lead that work. This is the most senior individual contributor role in our AI squad. Your role is to define what good output looks like, how we measure it, and what a change has to demonstrate before it ships. 

What makes this role unique is that you will be designing this tool directly with our Consultant Child Psychiatrists who understand the potential complexity of each case. You can test each hypothesis with our data bank of thousands of completed assessments, then get immediate feedback from 100+ clinical team members on each live adjustment. Your subject matter experts are in our London office working alongside you, not on the other side of a contract.

You will work as a member of the AI squad, which sits within our wider product and engineering function alongside our expert clinicians, delivering:

• A metric framework that connects Paloma's user experience and clinical efficiency goals to the technical metrics engineers optimise against day-to-day, and reporting against it to the Leadership Team
• A clinician-defined quality standard for AI output, developed with our Consultants through structured review of real cases, and the evaluation infrastructure that measures against it
• Experimental discipline across the AI pipeline, so that we know which changes caused which improvements and can attribute failures to specific stages
• Architectural decisions on the AI pipeline that serve the near-term roadmap without foreclosing where we want to be in two years

As the AI team grows, this is the natural route into leading it.

Does this sound like you?

• You treat measurement as the first discipline, not something added after the build. You are uncomfortable shipping a change you cannot demonstrate was an improvement.
• You have defined metrics in a domain where none existed, and you are comfortable with the ambiguity that involves – including the judgement of when a rough measure now beats a perfect one later.
• You can spend an hour with a domain expert and come away with something an engineer can build against, and explain to that same expert what the system can and cannot yet do. The translation matters in both directions.
• You have raised the standard of how a team works, not only what it shipped. Colleagues adopt your habits because your reasoning is convincing.
• You have strong judgment about what not to build, and you are willing to argue for it.
• You take the responsibility of building for a clinical setting seriously and would rather get something right than ship it fast.
• You have high agency, spotting what needs to be done and driving it through without waiting to be asked.
• You embrace winning as a team, and enjoy positive, collaborative cultures

Responsibilities:

• Own the metric framework for Paloma's AI systems: translate operational and product goals into technical metrics the squad optimises against, keep it current as the roadmap moves, and report progress against it to the Leadership Team
• Define, with our Consultant Child Psychiatrists, what a complete and accurate AI output looks like for different case types, and what the threshold for evidence sufficiency is at each stage of the assessment pathway
• Set the evaluation standard for the squad: specify what evaluation must exist for each part of the pipeline and what a change must demonstrate before it ships, working in close partnership with the AI Engineers who build and run those pipelines
• Lead structured error analysis across the pipeline, attributing failures to specific stages rather than treating quality as a single aggregate number
• Own machine learning architecture decisions, including model selection, agent and harness design and document the reasoning and evidence behind them
• Establish metric-driven development as the default across the squad and the wider engineering team, through mentoring, technical review, and championing the practice with product and clinical colleagues
• Document technical decisions and specifications in line with healthcare regulatory requirements

Qualifications and experience:

Required:

• Substantial experience building machine learning systems within companies that reached real users, in