Senior ML Engineer - Email Security

Jobgether; France; Remote; EUR 90,000 - 120,000; Full time

Benefits offered by this job

Fully Remote Work

Flexible Working Hours

Paid Vacation: 24 days

Public Holidays: 10 days

Sick Leave: Unlimited

Healthcare Support: Private insurance

Wellness Benefits: Co-working & gym

Learning and Development: Education预算

Innovation Rewards: Patented ideas

Technical Challenges: ML and security

Job summary

Jobgether is seeking a Senior ML Engineer to advance email security by training and deploying spam-detection models and building a lightweight Go-based inference engine. You will own end-to-end ML lifecycle from data preparation to production releases, collaborating with partner engineers to improve accuracy and performance.

The role emphasizes hands-on transformer training, CPU-efficient inference, and strong documentation and collaboration to deliver scalable, reliable defenses against

Qualifications

• Five years of experience delivering detection or classification systems where false positives have meaningful consequences.
• End-to-End ML ownership including corpus construction, threshold selection, error analysis, and release decisions.
• Hands-on experience training transformers with PyTorch, including tokenizers, masked-language pre-training, fine-tuning, ONNX export, and CPU inference performance measurement.
• Strong practical knowledge of Linux, shell scripting, and Python, including working with shared servers and GPU resources.
• Regular use of AI coding agents and development tools to support engineering productivity.
• Written English proficiency at B2 level or higher, with strong technical documentation and code-review skills.
• Ownership and judgment to analyze model performance, release decisions, and balancing detection vs false positives.

Responsibilities

• Train, evaluate, and release spam-detection models and maintain training corpora and thresholds.
• Develop production components of a new email-filtering engine in Go, including the inference service and integrations.
• Investigate misclassifications and implement fixes via data or model retraining.
• Balance detection rates with false positives to avoid blocking legitimate messages.
• Maintain corpus management tools and training infrastructure for reliable experimentation.
• Review classifier, rule, and model changes to ensure smooth releases.

Skills

Model Training and Deployment

Production ML Engineering

Detection Quality Improvement

Model Performance Optimization

Training Infrastructure and Data Tool

Code and Model Reviews

Technical Collaboration

Documentation and Knowledge Sharing

Independent Delivery

Tools

Go

Rust

Python

Linux

Shell scripting

ONNX

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior ML Engineer - Email Security based remotely in France.

The Senior ML Engineer will help develop and deploy advanced machine learning technology to protect email infrastructure against spam, phishing, and other evolving cyber threats. You will work on a production-scale security solution that already filters email across approximately 5,000 hosting servers. The role combines hands‑on transformer model training with systems engineering to build lightweight, efficient inference technology that runs on customer CPUs with minimal resource consumption. You will take ownership of the complete detection lifecycle, from dataset preparation and model evaluation to production releases and ongoing quality improvements. Working closely with an experienced engineering partner, you will help modernize the existing filtering pipeline and make releases more reliable and scalable. This remote‑first opportunity is ideal for an engineer who enjoys solving challenging security problems and delivering measurable improvements in real‑world environments.

Accountabilities:

• Model Training and Deployment: Train, evaluate, and release spam‑detection models, including building and maintaining training corpora, selecting decision thresholds, analyzing precision and recall, and determining whether models are ready for production.
• Production ML Engineering: Develop the production components of a new email‑filtering engine in Go, including the inference service, mail‑filter integrations, tokenization, and streaming message parsing.
• Detection Quality Improvement: Investigate customer‑reported spam and phishing incidents, trace detection failures, and implement appropriate fixes through rules, dataset improvements, or model retraining.
• Model Performance Optimization: Balance detection rates against false‑positive risks, ensuring legitimate messages are not unnecessarily blocked while malicious content is effectively identified.
• Training Infrastructure and Data Tooling: Maintain the corpus management tools and training infrastructure required to support reliable experimentation and model releases.
• Code and Model Reviews: Review and approve classifier, rule, and model changes to improve quality, share technical knowledge, and ensure releases can proceed without depending on a single engineer.
• Technical Collaboration: Help partner engineers understand classification outcomes, explain model scoring behavior, and provide clear technical guidance when questions arise.
• Documentation and Knowledge Sharing: Write technical specifications, handbooks, merge‑request descriptions, and post‑mortems that make decisions understandable and support effective collaboration.
• Independent Delivery: Progress toward defined milestones, including independently shipping a rule‑model release during the first month, delivering a component of the new engine to staging by month three, and supporting a model release independently by month six.

Requirements:

• Production Detection Experience: At least five years of experience delivering detection or classification systems where false positives have meaningful consequences, such as anti‑spam, anti‑phishing, anti‑malware, fraud prevention, or abuse detection.
• End‑To‑End ML Ownership: Experience independently taking at least one machine learning classifier into production, including corpus construction, threshold selection, error analysis, and release decisions.
• Transformer Training Expertise: Hands‑on experience training transformers with PyTorch, including tokenizers, masked‑language pre‑training, fine‑tuning, ONNX export, and CPU inference performance measurement.
• Systems and Development Skills: Strong practical knowledge of Linux, shell scripting, and Python, including working with shared servers and GPU resources.
• AI‑Assisted Development: Regular use of AI coding agents and development tools to support engineering productivity.
• Written Communication: English proficiency at B2 level or higher, with particularly strong written communication skills for technical documentation, specifications, and code reviews.
• Ownership and Judgment: The ability to analyze model performance, make evidence‑based release decisions, investigate difficult problems, and balance detection effectiveness with the cost of false positives.

Additional experience that would be valuable includes:

• Email Protocols and Infrastructure: Knowledge of SMTP, MIME, transfer encodings, SPF, DKIM, and DMARC, as well as how Exim, Postfix, and Dovecot integrate with email filters.
• Embedded Machine Learning: Experience building models designed to operate within constrained environments.
• Systems Programming: Familiarity with Go or Rust, particularly for parsers, streaming systems, concurrency, and memory‑constrained applications.
• Spam Filtering Frameworks: Experience with rspamd or SpamAssassin internals, including rules, composite rules, Lua plugins, and mass‑check scoring.
• Large‑Scale Dataset Management: Experience working with multi‑million‑sample corpora, labeling pipelines, and annotation tools.
• Model Optimization: Knowledge of quantization, distillation, lightweight model architectures, and latency optimization for resource‑constrained hardware.
• AI‑Assisted Annotation: Experience using LLM‑based labelers or validators to scale data annotation and quality assurance.
• Hosting Platforms: Familiarity with hosting control panels such as cPanel, Plesk, or DirectAdmin.

Benefits:

• Fully Remote Work: Work remotely with the flexibility to organize your schedule and work from different locations worldwide, subject to role and hiring arrangements.
• Flexible Working Hours: Autonomy to structure your working day around your productivity and commitments.
• Paid Vacation: 24 days of paid vacation annually.
• Public Holidays: 10 days of national holidays.
• Sick Leave: Unlimited sick leave.
• Healthcare Support: Financial compensation toward private medical insurance.
• Wellness Benefits: Reimbursement for co‑working spaces and gym or sports activities.
• Learning and Development: An education budget and a focus on ongoing professional growth.
• Innovation Rewards: The opportunity to receive a reward for developing an innovative idea that can be patented.
• Technical Challenges: Opportunities to work on demanding machine learning, cybersecurity, and production engineering projects with measurable real‑world impact.

We appreciate your interest and wish you the best!

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre‑contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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