Senior ML Engineer - Email Security

Jobgether; Australia; Remote; AUD 140,000 - 190,000; 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 stip

Learning and Development budget

Innovation Rewards

Technical Challenges

Job summary

Jobgether invites applications for a Senior ML Engineer - Email Security, remote in Australia. The role focuses on developing and deploying machine learning to protect email infrastructure from spam, phishing, and other threats, with a production-scale pipeline across thousands of hosting servers.

You will train transformers, build efficient inference services in Go, and own the complete detection lifecycle from data prep to production releases.

Qualifications

• Five years of production detection or classification experience with high impact.
• End-to-end ML ownership from dataset to production release.

Responsibilities

• Train, evaluate, and release spam-detection models and manage training corpora.
• Develop production components of a new email-filtering engine in Go.
• Investigate incidents, fix through data or model retraining.
• Balance detection rates with false positives in production.
• Maintain corpus management tools and training infrastructure.
• Review classifier, rule, and model changes to ensure quality.
• Explain model outcomes to partner engineers and provide guidance.
• Write technical documentation and post-mortems.
• Ship a rule-model release in month 1 and a staging component by month 3.

Skills

ML model training

Transformer models

Python

English communication

Tools

Go

Python

Linux

Docker

Go/Rust development

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 Australia.

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.