Senior/Lead Machine Learning Engineer

Mimica

. Transform raw recorded data into clean, structured, anonymized data for downstream teams .

Posted 10/22/2026full-timeRemote • United KingdomSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in transforming raw data into structured formats and optimizing machine learning pipelines for efficiency and accuracy. Proficient in evaluating and fine-tuning models, with strong Python programming skills and the ability to communicate technical concepts effectively.

Highest-signal resume keywords

Machine Learning Pipeline OwnershipLLM Evaluation and BenchmarkingPython ProgrammingData Transformation and AnonymizationExperimental Design and Statistical Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData ScienceExperimental DesignStatistical SignificanceLLM ApplicationModel DevelopmentData SplitsMetricsBias EvaluationOCR Processing

Soft Skills

Independent Problem SolvingEffective CommunicationTechnical Trade-off Explanation

Tools & Technologies

AI Coding ToolsProduction PipelinesEvaluation SetsBenchmarksTask-specific Models

Industry Keywords

AnonymizationDeep LearningStartup EnvironmentScale-up EnvironmentImage Processing

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Transform raw recorded data into clean, structured, anonymized data for downstream teams
Own key parts of ML pipelines end to end
Improve pipeline accuracy, cost efficiency, and trustworthiness
Build evaluations and LLM judges
Run experiments and turn promising results into validated production improvements
Evaluate and fine-tune open-source models
Build smaller task-specific models
Own, debug, and improve production pipelines
Design and iterate on LLM evaluations and LLM-as-a-judge systems, including datasets, metrics, and benchmarks
Experiment with prompts, pipeline design, model choice, and inputs such as OCR text versus image plus OCR
Drive LLM efficiency improvements by comparing models, restructuring steps, and replacing LLM calls with smaller purpose-built models
Expand the anonymization pipeline by evaluating and adapting existing models
Work with Python and quality engineers to productionize experiments
Share responsibility for shipped production code
Document findings in experiment reports and design documents

Requirements

What you’ll need

Solid machine learning/data science background, including experimental design, data splits, metrics, bias, and statistical significance
Hands-on experience applying LLMs in production or near-production settings, especially evaluating them
Experience building eval sets, LLM judges, benchmarks, and iterating on prompts and pipelines
Comfortable exploring, troubleshooting, and transforming messy data into measurable improvements
Strong Python and good engineering skills
Clear, readable, tested code suitable for production
Ability to work independently on ambiguous problems
Effective use of AI coding tools with accountability for shipped output
Clear spoken and written English
Ability to explain technical trade-offs to technical and non-technical audiences
Experience with classical ML or deep learning model development (nice to have)
Experience with image/OCR pipelines or face-related anonymization (nice to have)
Experience shipping end-to-end ML features in a startup or scale-up environment (nice to have)

Benefits

Comp & perks

Generous compensation + stock options
Distributed work: Work from anywhere - fully remote, in our hubs, or a mix
Company-issued laptop
Remote setup stipend
Co-working budget
Flexible schedules and location
Ample paid time off, in addition to local public holidays
Enhanced parental leave
Health & retirement benefits
Annual learning & development budget
Annual workaways
Regular virtual & in-person socials
Opportunity to contribute to groundbreaking projects that shape the future of work