
Security Engineer, Detection & Response
San Francisco, CAJob$300–350K/yrPosted 2 days agoStill listed 1 day ago
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At a glance
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Job overview
Thinking Machines Lab is seeking a detection and response engineer to build threat-detection pipelines, lead incident response, and run threat intelligence focused on adversaries targeting frontier AI labs. The role partners with security, infrastructure, research, and product teams to scale defenses. It is based in San Francisco and includes participation in an on-call rotation for security incidents.
Skills & qualifications
Skills
Qualifications
Benefits
Full job description
About Thinking Machines The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it.
About the Role We’re looking for a detection and response engineer to build how we find, investigate, and stop threats against our company, platforms, models, and data. You’ll own the pipelines that turn raw telemetry into high-fidelity detections, lead the response when real incidents happen, and run a threat intelligence practice that keeps our defenses focused on the adversaries who actually target frontier AI labs. You’ll partner closely with security, infrastructure, research, and product teams so that detection and response scale with the company instead of slowing it down.
What You’ll Do
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Lead incident response end-to-end: triage, scoping, containment, eradication, recovery, and blameless postmortems that turn lessons into new detections and controls.
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Design, build, and operate the security data pipelines and tooling that power detection and response: collecting, normalizing, enriching, and storing telemetry from cloud, Kubernetes, identity, endpoint, SaaS, and application sources.
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Author, deploy, and maintain high-fidelity detections across SIEM, endpoint, cloud, and security tooling
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Build and run our threat intelligence practice: track actors and campaigns relevant to AI labs such as espionage, model weight theft, supply chain compromise, and credential abuse. Turn that intel into hunts, detections, and hardening recommendations.
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Proactively hunt for threats across our environment, and build automation and response tooling: enrichment, playbooks, automated containment that shortens time to detect and respond.
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Collaborate with researchers and product teams to detect AI-specific threats in production.Develop and exercise incident response plans, on-call processes, and tabletop exercises with engineering, research, legal, and leadership.
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Participate in an on-call pager rotation for security incidents.
Skills and Qualifications Minimum qualifications
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Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
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Proficiency in Python or Rust for building data pipelines, detections, and internal tools.
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A track record responding to real security incidents in production environments, including leading investigations and coordinating across teams under pressure.
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Hands-on experience building and operating security data pipelines at scale: log ingestion, parsing and normalization, enrichment, and storage and query in a SIEM or data lake (e.g., Splunk, Elastic, Panther, BigQuery, Snowflake, or ClickHouse).
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Experience producing and consuming threat intelligence: tracking adversary TTPs, mapping them to frameworks like MITRE ATT&CK, and translating intel into detections and hunts.
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Strong understanding of cloud, AWS, GCP, or Azure, Kubernetes, and identity provider telemetry, and how attackers move through these environments.
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Familiarity with host and network forensics, including endpoint telemetry, memory and disk analysis, and network logs.
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Comfort operating across the stack and owning projects end-to-end.
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Clear communication: incident reports, intel briefs, and postmortems for both technical and non-technical audiences.
Preferred qualifications
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Detection and response experience at an AI lab, cloud provider, or other high-threat environment targeted by sophisticated actors.
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Experience detecting AI-specific abuse, such as prompt injection, model extraction, or anomalous API usage.
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Familiarity with ML infrastructure telemetry: GPU clusters, training jobs, and model artifact storage.
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Experience with stream processing (e.g., Kafka, Flink, Vector) and cost-aware log management at high volume.
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Malware analysis or reverse engineering experience.
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Talks, blogs, open-source detection content, or published threat research.
Logistics
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Location: This role is based in San Francisco, California.
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Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $300,000 - $350,000 USD.
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Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
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Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
How this job compares
Set against the 4,152 open Security Engineer jobs on Olive Jobs, counted October 29, 2026.
- Pay
- This posting$300–350K/yr
- California median$165–248K/yr616 salary ranges
- US median$132–208K/yr3,384 salary ranges
This range's midpoint, $325K, is 58% above the California median midpoint ($206K) — in the top quarter of Security Engineer salary ranges in California.
- Openings
- Olive Jobs lists 757 open Security Engineer jobs in California and 4,152 across the US. 8% of them nationwide are listed as remote or flexible.
- Experience
- Security Engineer postings that state a minimum ask for 5 years of experience at the median; the middle half ask for 4 to 8 years (3,590 postings).
- Education
- 1,543 of the 4,152 open Security Engineer jobs (37%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on October 29, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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