Interested in this AI/ML Engineer role at Bitdeer Technologies Group?
Apply Now →Skills & Technologies
About This Role
About Bitdeer Technologies Group
Bitdeer is a world\-leading technology company for AI and Bitcoin mining infrastructure.
Bitdeer is committed to providing comprehensive Bitcoin mining solutions for its customers and building AI computational infrastructure to support the AI revolution. Bitdeer handles complex processes involved in computing such as equipment procurement, transport logistics, data center design and construction, equipment management, and daily operations. Bitdeer also offers advanced cloud capabilities to customers with high demand for artificial intelligence.
Headquartered in Singapore, Bitdeer has deployed data centers across multiple countries, including the United States, Norway, Bhutan, and Ethiopia.
To learn more, visit https://ir.bitdeer.com/ (https://ir.bitdeer.com/%3C/p%3E%3Cp%3E%3C/p%3E%3Cp%3E%3Cstrong%3EJob)
About the Role
As Bitdeer AI Cloud's first dedicated security leader for the Americas, you will own the full\-stack security and 7×24 security operations of AI Data Centers (AIDCs) across California, Tennessee, Washington, and future locations.
This is a deeply hands\-on technical operations role. You will personally lead detection engineering, incident response, host/network hardening, while also handling US customer incident response, law enforcement requests, and cross\-time\-zone coordination with our Singapore HQ.
Core Mission: Despite the 12–16 hour time difference with Singapore HQ, you will ensure the GPU compute business across three Americas AIDCs runs securely across physical, network, host, virtualization, and customer operations layers, while driving incident MTTR to industry\-leading levels.
Key Responsibilities
1\. AIDC Security Operations \& 7×24 Incident Response
- Regional Ownership: Serve as the primary on\-call security lead for the Americas region. Own 7×24 alert triage, incident response, and root cause analysis for AIDCs in CA, TN, WA, and beyond. Act as the primary security decision\-maker during Americas business hours (PST 09:00–18:00\) when Singapore HQ is offline.
- Hands\-on IR: Personally drive the response to high\-severity incidents (P0/P1\) including GPU cluster cryptojacking, ransomware, data exfiltration, and tenant escape scenarios. Lead the full forensics, containment, and recovery cycle.
- Playbook \& Automation: Build and maintain Americas regional incident response playbooks and runbooks. Collaborate with the global SecOps team on SIEM detection rules, SOAR automation, and IR tabletop exercises.
- Escalation \& Communication: Lead customer security incident response—handle customer tickets, engage customer security teams, and coordinate with Sales and Customer Success on external communications. Serve as the Americas escalation interface, coordinating decisions with Singapore HQ, Legal, and business teams during major incidents.
2\. Detection Engineering \& Threat Hunting
- Rule Development: Personally write SIEM detection rules (Wazuh, Splunk, Elastic SIEM, or equivalent) covering typical GPU cloud attack scenarios: anomalous GPU utilization/cryptojacking, anomalous SSH logins, container escape, Kubernetes API abuse, and InfiniBand network anomalies.
- Framework Alignment: Design detection coverage assessments based on the MITRE ATT\&CK Cloud Matrix and Container Matrix. Proactively identify and close visibility blind spots.
- Threat Hunting: Lead hypothesis\-driven threat hunting activities. Conduct at least two structured hunting campaigns per month, producing comprehensive hunting reports and new detection rules.
- Cloud\-Native Detection: Design runtime detection capabilities using eBPF tools (Tetragon, Falco, Cilium) to complement traditional HIDS detection blind spots.
- Detection\-as\-Code: Operationalize detection\-as\-code practices in the Americas region, including version\-controlled detection rules, CI/CD pipelines, unit testing, and coverage metrics.
3\. AIDC Infrastructure Security Hardening
- Pre\-Production Assessment: Lead pre\-production security readiness assessments for all Americas AIDCs. This covers perimeter networks, OOB management networks, BMC/IPMI hardening, KVM/QEMU virtualization baselines, GPU isolation validation (MIG/vGPU/Time\-Slicing), and InfiniBand SM\-key/M\-key/P\-key configuration reviews.
- Host Hardening: Personally drive host hardening initiatives, including Linux baselines (CIS Benchmarks), auditd configuration, SSH hardening, privileged account management, and firmware/microcode CVE tracking.
- Platform Collaboration: Partner with the Platform Engineering team to deploy eBPF\-based runtime security monitoring (Tetragon/Falco) to cover container escape and anomalous syscall detection.
- Vulnerability Management: Track CVEs for NVIDIA GPU drivers, CUDA, NCCL, UFM, BMC firmware, and other critical components. Lead the Americas regional vulnerability response and patch window negotiations.
- Access Control: Lead Americas regional IAM and privileged access management by deploying jump host solutions (Teleport / Boundary), JIT access, and privileged session recording/auditing.
4\. Network Security \& Perimeter Defense
- Perimeter Security: Lead the configuration and operations of perimeter firewalls, IPS, and WAF for all three Americas AIDCs.
- DDoS Mitigation: Engage DDoS scrubbing services (Cloudflare Magic Transit, Arbor, or equivalent) and build robust Americas regional DDoS response plans.
- Traffic Analysis: Establish east\-west traffic baselines based on NetFlow / IPFIX to identify anomalous traffic patterns (data exfiltration, C2 communication, lateral movement).
- Network Controls: Configure BGP RPKI, source address validation (uRPF), and other network\-layer security controls.
- Traceability: Plan and deploy traffic analysis solutions (e.g., Panabit NTM) at Americas AIDCs to enable full traffic traceability at physical boundaries.
5\. Customer Incident Response \& Law Enforcement Requests
- Abuse \& Tickets: Serve as the security incident response interface for Americas customers. Respond to customer\-submitted security tickets, abuse complaints (cryptomining, unauthorized scanning, illegal content), and incident notifications.
- Legal Liaison: Handle US law enforcement requests (FBI, DEA, Secret Service, local police) including subpoenas, search warrants, and preservation orders. Collaborate closely with Legal to respond within statutory windows.
- SLA Tracking: Establish Americas regional customer security incident SLA tracking and post\-incident review mechanisms.
6\. Cross\-Time\-Zone Coordination \& Regional Security Construction
- HQ Sync: Establish seamless security collaboration mechanisms between the Americas and Singapore HQ via daily handoffs, weekly syncs, incident bridges, and on\-call escalation paths.
- Compliance Support: Serve as the Americas regional compliance support interface. Partner with the Singapore GRC Manager to provide the evidence collection and control implementation needed for SOC 2 US scope expansion.
- Community Engagement: Represent Bitdeer AI Cloud Security within local US security communities and industry events (BSides, DEF CON, Cloud Security Alliance US).
Job Requirements
- Education: Bachelor's degree or higher in Computer Science, Cybersecurity, Computer Engineering, or a related technical field.
- Experience: 10\+ years of hands\-on information security experience, with at least 5 years strictly focused on cloud infrastructure / IaaS / data center security technical operations roles (not pure management or documentation roles).
- Incident Command: Deep incident response experience as an Incident Commander, having successfully led at least 5 P0/P1 security incidents end\-to\-end. Thoroughly familiar with the NIST SP 800\-61 IR process.
- Technical Depth: Deep expertise in Linux system security, network protocols, TCP/IP, virtualization (KVM/QEMU), and container/Kubernetes security.
- SIEM \& Rules: Hands\-on experience with at least one mainstream SIEM platform (Wazuh / Splunk / Elastic SIEM / Sentinel) and the ability to independently write detection rules. Familiarity with the SIGMA rule format is required.
- Frameworks: Familiar with the MITRE ATT\&CK Framework (Cloud Matrix and Container Matrix) with a proven ability to design detection coverage assessments.
- Automation \& Code: Strong scripting and programming skills: Python (Required) \+ Shell (Required); *Go or Rust are highly preferred*. Ability to independently develop security tools and automation scripts.
- Cloud\-Native Tech: Familiarity with the eBPF technology stack (Tetragon / Falco / Cilium) and a strong understanding of its application in cloud\-native runtime security.
- Infrastructure as Code: Familiarity with at least one IaC tool (Terraform / Ansible) and standard Git workflows to codify security configurations.
- Certifications: At least one of the following industry certifications is required: GCIH, GCIA, GCFA, OSCP, CISSP, CCSP.
- Language Fluency: Professional fluency in both English and Mandarin Chinese is required. Must be able to communicate effectively in English with US customers, MSSPs, law enforcement, and auditors, and in Mandarin with the Singapore HQ team and management for complex technical discussions and strategic reporting.
- Scheduling: Willingness to accept irregular working hours. Must participate in a 7×24 on\-call rotation during major incidents and conduct daily cross\-time\-zone coordination with Singapore HQ (SGT).
\-
*Bitdeer is committed to providing equal employment opportunities in accordance with country, state, and local laws. Bitdeer does not discriminate against employees or applicants based on conditions such as race, color, gender identity and/or expression, sexual orientation, marital and/or parental status, religion, political opinion, nationality, ethnic background or social origin, social status, disability, age, indigenous status, and union.*
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 2,799 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Bitdeer Technologies Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $175,000 based on 11,128 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $159,385.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $159,385; Senior: $227,500; Director: $242,000; VP: $250,000.
Bitdeer Technologies Group AI Hiring
Bitdeer Technologies Group has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Massillon, OH, US, Austin, TX, US, VA, US.
Location Context
Across all AI roles, 16% (460 positions) offer remote work, while 2,318 require on-site attendance. Top AI hiring metros: New York (2,241 roles, $208,300 median); San Francisco (1,822 roles, $252,000 median); Los Angeles (1,611 roles, $188,900 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 2,799 open positions tracked in our dataset. By seniority: 98 entry-level, 1,283 mid-level, 1,092 senior, and 326 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (460 positions). The remaining 2,318 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 30 roles); AI Safety ($274,200 median, 43 roles); Research Engineer ($260,000 median, 387 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 2,799 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,978), AI Software Engineer (197), Data Scientist (195). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (98) are outnumbered by mid-level (1,283) and senior (1,092) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 326 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (460 positions), with 2,318 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $252,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,433 postings), Aws (840 postings), Rag (663 postings), Azure (639 postings), Gcp (537 postings), Pytorch (445 postings), Prompt Engineering (418 postings), Claude (396 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.