Senior ML Engineer - Malware Detection (remote work)

• Malware samples from production servers, with verdicts and analyst decisions
• Years of signatures and detection rules
• Customer-reported false negatives and false positives
• Runtime behavior of code on live sites
• Web-attack traffic
• File metadata and how files spread across servers
• Cleanup outcomes, including what came back

The goal is 10x more malware detected at the same or lower false-positive rate. Hand-written rules and faster pipelines will not get there; models trained on our data might.

• →Decide which files on a server deserve a closer look, within tight CPU and memory budgets
• →Tell malicious code from benign at fleet scale and a sensible cost per file
• →Generate detection signatures automatically from new campaigns
• →Catch malware that only looks malicious in context, such as phishing pages and script injections
• →Route each file to the right detection engine
• →Make cleanup precise so sites stay clean afterwards
• →Prove each model improves detection before it ships

You get an experimentation track, GPUs, and a direct line to leadership; in return, you bring numbers, not opinions.

• Remote-first, async across UTC−5 to UTC+8. Decisions are written down (ADR-Lite) and public by default.
• Hardware follows evidence: Each training direction starts with dedicated GPUs on our own bare metal; leadership has committed to fund more when the case is made with data.
• Agentic engineering: Claude and Codex are the default tools, wired to GitLab, Jenkins, Redash, Jira, and Slite through MCP servers. We look at how engineers work with agents — context, specificity, review quality — not only at output.
• Close to ground truth: You work daily with malware analysts, detection and infrastructure engineers, and the rest of our ML team.
• Ship gates: No model or build goes out if it raises false positives or false negatives on our past-error sets. Blameless post-mortems within 24 hours.

You own the path from raw samples to a model in production, and the numbers that prove it helped.

• First month: A first model trained on production data and measured against today's engines — suspicious-file triage or a metadata model. The evaluation harness in place: splits by time and campaign so families don't leak, and a regression set from past FPs and FNs.
• By month 3: Several models in production: some in shadow mode building evidence, some live and producing real detections, all gated on the past-error regression set. A training setup that keeps its GPUs busy. A written proposal (ADR) for the detection cascade, with cost per file and hardware needs.
• By month 12: Models are a main source of new detections, reported as share of new detections and FN-rate trend. Retraining on fresh analyst and FN labels on a fixed cadence. Generated signatures inside the analyst workflow. Two or three parallel training directions that other engineers can run with your tooling.

• 5+ years building ML systems that ran in production, including 2+ in an adversarial domain: security, abuse, fraud, or spam.
• You have trained and fine-tuned transformer models yourself — data preparation, tokenizing code, LoRA/QLoRA or full fine-tunes, distillation, quantization — not only called hosted APIs.
• Classic ML depth: gradient boosting, linear models on sparse features, calibration, class imbalance, and judgement on when these beat an LLM.
• Evaluation discipline: leakage-free splits by time and family, precision at a fixed false-positive budget, regression sets, and an honest account of why production will differ from offline.
• Inference cost engineering: you have measured throughput and latency and made a model fit a CPU or memory budget (ONNX, llama.cpp, vLLM, int8/int4).
• Strong Python, PyTorch and the Hugging Face stack; SQL on large analytical tables (ClickHouse or similar).
• Familiarity with PHP or JavaScript, or willingness to pick it up working with our malware analysts.
• You use AI coding agents daily and can show how.
• English at upper-Intermediate (B2) level or higher, with particularly strong written communication for async work.

• Web malware, webshells, phishing kits or WordPress security experience.
• Models over code: AST or token features, code LLMs, graph models on program structure.
• Malware clustering and similarity analysis.
• Multi-GPU training on your own bare metal; MLflow or Weights & Biases; dataset versioning.
• Go or Rust, for on-server inference.
• Published work or talks on ML for security.

• A focus on professional development.
• Interesting and challenging projects.
• Fully remote work with flexible working hours, that allows you to schedule your day and work from any location worldwide.
• Paid 24 days of vacation per year, 10 days of national holidays, and unlimited sick leaves.
• Compensation for private medical insurance.
• Co-working and gym/sports reimbursement.
• Budget for education.
• The opportunity to receive a reward for the most innovative idea that the company can patent.

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