[Hiring] Senior Machine Learning Engineer @Wallarm

Role Description

You'll be the first dedicated ML engineer on the team, working closely with engineers, security researchers, and DevOps. This is a senior IC role with a clear path to technical leadership - we plan to grow the ML function around this hire.

• Build the ML stack from the ground up:
  • Design and implement the data pipelines, feature extraction, model training, and serving infrastructure needed for production-grade anomaly detection.
• Detecting anomalies in API traffic:
  • Your first major outcome: build a system that identifies malicious behavioral patterns across client sessions with high precision and recall, trained per-client.
• Own the full lifecycle:
  • From raw data exploration and feature engineering through model development, evaluation, deployment, and continuous monitoring. No handoffs to a separate "productionization" team.
• Design experiments and metrics:
  • Build offline evaluations, define detection-quality metrics, and monitor for false positives, drift, and adversarial adaptation.
• Work with text and structured behavioral data:
  • Extract signals from API sessions, request sequences, payloads, and traffic metadata using NLP and statistical techniques.
• Leverage LLMs where they add value:
  • Explore embedding-based models and LLM-augmented approaches for signal enrichment, classification, and explainability.
• Shape the technical direction:
  • Document findings, present to cross-functional teams, and help define the ML roadmap as the team grows.

Qualifications

• 5+ years in Applied ML or ML Engineering with production deployment experience (not research-only backgrounds).
• Strong NLP / text data experience - hands-on work with text classification, pattern extraction, tokenization, embeddings, or similar.
• Proficiency in Python and production-grade systems (APIs, data pipelines, model serving).
• Solid data engineering skills - experience building ETL/data pipelines, working with batch and streaming data, and understanding the full ML data lifecycle (DAGs, data versioning, feature stores).
• Deep hands-on experience across ML fundamentals: classification, anomaly detection, clustering, statistical methods.
• Comfort with imperfect data - noisy labels, class imbalance, evolving distributions.
• End-to-end ownership mindset - ability to take a problem from raw data to production deployment.
• Strong experimentation skills: prototype fast, design rigorous evaluations, measure outcomes, reason about trade-offs (cost, quality, latency).

Requirements

• Experience in domains where adversaries actively adapt to detection (fraud, bot mitigation, abuse prevention, spam).
• Familiarity with ML lifecycle tooling: experiment tracking (MLflow, W&B), model versioning (DVC), weak-supervision tools (Snorkel, cleanlab), drift monitoring.
• Experience with big data / streaming stacks (Spark, Kafka, BigQuery) or cloud ML platforms (AWS SageMaker, GCP Vertex).
• Background in security research or threat intelligence (not required - domain context can be learned).

Who Thrives Here

• You're a full-stack ML engineer - equally comfortable building a data pipeline and tuning a model, designing an experiment and deploying it to production.
• You've built from scratch before - you know what it takes to go from "we have data and ideas" to "we have a working detection system."
• You're energized by ambiguity and ownership - this isn't a well-scoped ticket queue, it's an open problem space where you define the path.
• You're ready to grow into leadership - mentoring engineers, shaping technical strategy, and owning the ML roadmap as the team scales around you.
• You leverage modern tools (AI-assisted development, LLM-augmented workflows) to move faster without cutting corners.