Staff ML Engineer: Time Series & Anomaly Detection

Nominal; Greater London; On-site; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Health insurance

Unlimited PTO

Free lunch

Dev stipend

Company retreat

Job summary

Nominal is seeking a Staff Machine Learning Engineer, Time Series & Statistical Methods, to empower our agents with real numerical tools for hardware data. You’ll guide ML strategy for high-rate telemetry and testing campaigns, bridging classical methods and deep learning.

Join a collaborative, high-agency team building AI-native applications and agents. You’ll mentor engineers, ship reliable code, and help set the roadmap for ML at Nominal, from signals to forecasts across a diverse hardware

Qualifications

• 8+ years of applied ML or statistics with production systems on time-series or sensor data.
• Strong grounding in statistics, signal processing, anomaly detection, and forecasting.
• Hands-on deep learning with sequences and sensor data.
• Excellent software engineering ensuring reliable, tested code in production.
• Judgment on when classical methods beat neural nets and when to use learned models.
• Track record of setting technical direction across a team and raising the bar for engineers.
• Experience with modern AI coding agents (Claude Code, Cursor, Codex).

Responsibilities

• Build anomaly detection, change-point detection, and forecasting for high-rate test and fleet telemetry.
• Package these methods as agent tools for real feature engineering and statistics.
• Develop signal-processing and statistical methods that hold up on noisy, real-world data.
• Partner with evals to measure when a method is good enough for engineers.
• Set the technical bar and roadmap for ML at Nominal, including when to invest in learned models.

Skills

Time-series ML

Statistics

Signal processing

Forecasting

Anomaly detection

Deep learning

Transformers

Software engineering

Team leadership

Experimentation

Tools

Claude Code

Cursor

Codex

Job description

Nominal is seeking a Staff Machine Learning Engineer, Time Series & Statistical Methods, to empower our agents with real numerical tools for hardware data. You’ll guide ML strategy for high-rate telemetry and testing campaigns, bridging classical methods and deep learning.

Join a collaborative, high-agency team building AI-native applications and agents. You’ll mentor engineers, ship reliable code, and help set the roadmap for ML at Nominal, from signals to forecasts across a diverse hardware