Staff Machine Learning Engineer, Time Series & Statistical Methods - UK

Responsibilities

You will build and implement anomaly detection, forecasting, and signal-processing methods for high-rate hardware telemetry. Additionally, you will package these methods as agent tools to enable AI agents to perform reliable statistical analysis on complex physical systems.

Requirements

The role requires 8+ years of experience in applied machine learning or statistics with a focus on time-series or sensor data. Candidates must possess strong software engineering skills and a deep understanding of both classical statistical methods and modern deep learning techniques.

Skills

• Machine learning
• Statistics
• Time series analysis
• Signal processing
• Anomaly detection
• Forecasting
• Deep learning
• Transformers
• Representation learning
• Software engineering
• Python
• Telemetry analysis
• Sensor data
• Hardware intelligence
• Feature engineering

Benefits

• Unlimited PTO
• Sick leave
• Free lunch
• Snacks
• Coffee
• Professional development stipend
• Annual company retreat
• Hardware sensor stipend
• Employee-friendly equity terms

About this role

ABOUT NOMINAL

Engineers today are building some of the most ambitious machines humanity has
ever seen. They should have AI that’s purpose-built for them.

Nominal is building the Hardware Intelligence Platform that gives engineers
increased visibility into complex physical systems, helps them make better
decisions, and ultimately gets their products into the real world faster. It
brings together the systems, data, intelligence, and applications hardware
engineering requires to meet the AI moment. Hardware engineering teams use
Nominal to simulate, test, manufacture, and operate the physical systems that
keep the world moving, from spacecraft and autonomous vehicles to
next-generation energy systems and robotics.

Our customers span aerospace, defense, manufacturing, transportation, energy,
and robotics. They include Anduril, Shield AI, Wisk Aero, Pratt Miller
Motorsports, Antares, and Shinkei Systems, amongst others, alongside programs
within the U.S. Air Force and Navy. Our team draws from SpaceX, Palantir,
Anduril, Applied Intuition, Tesla, and other leading companies, united by a
common mission: giving hardware engineers the tools to build the future with
speed, safety, and confidence. We're a fast-moving team that owns problems
end-to-end, works across disciplines, and thrives at the intersection of
hardware and software.

Some of the world’s leading technology investors, including Sequoia, Lightspeed,
Founders Fund, General Catalyst, Lux Capital, and Insight Partners, are backing
Nominal’s ambition to change how machines are built and operated. We’re looking
for engineers who want to take on hard problems and help make a new generation
of world-changing hardware possible.

ABOUT HARDWARE INTELLIGENCE

We're the team behind Nominal's agents, AI-native applications, and MCP, and its
forward-leaning AI bets. Our mission is to unlock the bottlenecks of the
hardware lifecycle with AI. Our agents reason over physical reality, from
high-rate telemetry and test campaigns to designs and simulations, where real
test results are the ground truth their work is checked against. We believe
opinionated AI, built for the real work of hardware programs, will change how
the world engineers.

We're collaborative, iterative, and high-agency, and we're human-centered and
customer-focused. We build with the newest AI tools every day, and because those
tools keep changing, so do we: we stay curious and keep looking for the better
way. Our team spans data science and ML, distributed systems, search, and
knowledge systems, and we obsess over how agents can be genuinely useful to the
engineers who rely on them.

THE ROLE

As a Staff Machine Learning Engineer, Time Series & Statistical Methods, you'll
give our agents real numerical tools, because LLMs alone can't reason well over
millions of sensor samples, and you'll set Nominal's direction on ML for
hardware data, from classical methods to deep learning.

💼 WHAT YOU'LL DO

* Build anomaly detection, change-point detection, and forecasting for
high-rate test and fleet telemetry.
* Package these methods as agent tools, so our agents can run real feature
engineering and statistics instead of guessing.
* Develop signal-processing and statistical methods (frequency analysis, trend
fitting, run-to-run comparison) that hold up on noisy, real-world data.
* Partner with evals to measure when a method is good enough to put in front of
engineers.
* Set the technical bar and roadmap for ML at Nominal, including when, and
whether, to invest in learned models over classical ones.

🚀 WHAT YOU'LL BRING

* 8+ years in applied ML or statistics, with production systems on time-series
or sensor data.
* Deep classical grounding: statistics, signal processing, anomaly detection,
and forecasting.
* Hands-on deep learning: transformers, embeddings, and representation learning
for sequences and sensor data.
* Strong software engineering; your methods ship as reliable, tested code.
* Judgment about simple-versus-complex: you know when a well-chosen statistical
test beats a neural net, and when it doesn't.
* A track record of setting technical direction across a team and raising the
bar for the engineers around you.
* You build with modern AI coding agents (Claude Code, Cursor, Codex) every
day, and stay curious and open to better ways of working. The tools keep
changing, and so do we.

⚡️ NICE TO HAVE

* You've built anomaly detection or forecasting that drove real operational
decisions, in observability, predictive maintenance, or vehicle telemetry.
* You've worked with telemetry from aircraft, vehicles, energy systems, or
robots.
* You've shipped ML methods as tools that other systems or agents call, not
only as models.
* You've worked with the latest models beyond text: VLMs, VLAs, multimodal
transformers, or foundation models for robotics and physical systems.

✨ BENEFITS/PERKS

* 🏖️ Unlimited PTO and sick leave
* 🍽️ Free lunch, snacks, and coffee
* 🚀 Professional Development Stipend
* ✈️ Annual company retreat
* 🛠️ Hardware sensor stipend
* 📈 Employee-friendly equity terms
* 🏢 In-office 5 days per week

All qualified applicants will receive consideration for employment without
regard to race, color, religion, sex, sexual orientation, gender identity, or
national origin.

About Nominal

Software Development · 201-500 employees · headquartered in Austin, TX · founded 2022