Senior SOC Engineer Detection and AI Systems

About the company

Our client is a leading autonomous vehicle technology company operating at the cutting edge of self-driving software development. 

Role overview

We are not looking for a traditional SOC analyst. This role is about building detection systems, not clicking through alerts. You will work with large volumes of logs (cloud, application, network), design detection logic, and improve it over time. That includes both rule-based detection (SIEM/XDR) and data-driven approaches (statistical models, anomaly detection, simple ML where it makes sense). You should be comfortable treating logs as a data problem, not just a monitoring stream.

Important! This is not a role where you:

• sit in SIEM all day clicking alerts
• blindly add rules
• overcomplicate things with ML

This is a role where you: build detection systems that work in practice.

What you'll be doing

Detection & analytics

• Build and maintain detections across:
• application logs (auth, APIs, business logic abuse)
• cloud activity (CloudTrail, IAM, infrastructure changes)
• network telemetry (DNS, proxy, firewall, NetFlow)
• Work directly with data in platforms like Elasticsearch / Splunk / similar
• Write and tune detection logic (queries, correlation rules)
• Reduce noise — expect to spend time fixing false positives, not just adding new rules.

Working with data (this is a big part of the job)

• Build pipelines for:
• log ingestion
• normalization
• feature extraction
• Work with streaming or batch pipelines (Kafka / queues / scheduled jobs)
• Turn raw logs (e.g. CloudTrail) into structured data usable for detection.

Anomaly detection / ML (practical, not theoretical)

• Apply simple models where they add value (e.g. One-Class SVM or similar approaches)
• Focus on:
• behavior baselining
• anomaly scoring
• Tune models based on real output (false positives matter more than theory) 
• If ML does not improve signal — don't use it.

Automation & response

• Build response playbooks for common cases
• Automate where it actually saves time (not for the sake of it)
• Integrate with SOAR or write lightweight orchestration
• Improve:
• detection speed
• response consistency.

AI usage (realistic expectations)

• Use LLMs for:
• alert enrichment
• investigation summaries
• internal tooling
• Not building an "AI security platform"
• Focus on practical gains, not hype.

Threat hunting

• Run targeted hunts based on hypotheses
• Use internal data + external signals
• Identify gaps in logging and detection.

Requirements

• Solid experience with SIEM (Elastic, Splunk, Sentinel, etc.)
• Strong understanding of:
• cloud activity (especially AWS logs and IAM)
• network basics
• modern application behavior (APIs, auth flows)
• Comfortable working with large datasets (not afraid of raw logs)
• Python or similar for data processing
• Experience with automation (SOAR or custom scripts).

Nice-to-have (but not critical)

• Experience applying ML to logs (even simple models is enough)
• Familiarity with LLMs in tooling (not research)
• Experience with data pipelines (Kafka, Spark, etc.)
• MITRE ATT&CK mapping

What will make you effective here

• You don't trust alerts blindly
• You care about signal quality, not number of detections
• You can debug why a detection is noisy or useless
• You understand systems, not just tools
• You prefer building something once instead of repeating manual work.

How success looks like

• Fewer false positives
• Better visibility across systems
• Faster investigations
• Less manual work in SOC
• Detections that actually catch real issues