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About This Role
As a leader in Lawful and Location Intelligence, SS8 helps make societies safer. Our commitment is to extract, analyze, and visualize the critical intelligence that gives law enforcement, intelligence agencies, and emergency services the real\-time insights that help save lives. Our high performance, flexible, and future\-proof solutions also enable mobile network operators to achieve regulatory compliance with minimum disruption, time, and cost. SS8 is trusted by the largest government agencies, communications providers, and systems integrators globally.
Overview
We're looking for a highly motivated and visionary Full\-Stack Engineer with deep expertise in Artificial Intelligence to join our core engineering team. In this role, you will be instrumental in advancing our flagship platforms like Intellego® XT, Discovery, and LocationWise. You will design, build, and deploy complex end\-to\-end distributed systems and AI\-powered applications that process massive volumes of multi\-modal investigative data. Working at the intersection of full\-stack engineering and AI, you will build next\-generation web interfaces, geospatial intelligence tools, and generative AI pipelines that help law enforcement turn complex data into actionable clarity.
What You'll Work On
- Developing complex, end\-to\-end distributed web applications on cloud\-native platforms, primarily utilizing AWS.
- Integrating and deploying LLM and deep learning\-based applications, including voice analytics (such as OpenAI Whisper), document analysis, and image/video intelligence (e.g., artifact detection) using open\-source models.
- Building interactive, LLM\-driven frontends and complex UI applications involving dynamic dashboards and precise mapping/geolocation for our location intelligence products.
- Designing scalable, high\-throughput backend architectures utilizing Kubernetes, Kafka, Redis and distributed data stores.
- Utilizing AI\-assisted coding tools (like Claude Code, Codex, or Devin) to dramatically accelerate development, code reviews, and automated testing cycles.
- Collaborating with cross\-functional teams to engineer data platforms that fuse network intelligence, open\-source intelligence, and high\-precision location data into a "single pane of glass."
Key Responsibilities
- Architect, implement, and maintain high\-performance full\-stack applications using React (and related ecosystems) for the frontend and robust distributed technologies for the backend.
- Design, implement and troubleshoot microservices within Kubernetes\-based environments, ensuring high availability and fault tolerance.
- Develop and optimize scalable data processing pipelines utilizing distributed systems theory, caching (Redis), search engines (OpenSearch), event streaming (Kafka), and diverse database architectures (Graph, Relational, Document, Multimodal).
- Drive the operationalization of Deep Learning and Generative AI models into production environments to augment human analysts.
- Implement secure, hardened access controls via robust OAuth and Active Directory (AD) integrations to maintain evidentiary integrity and auditability.
- Lead by example in modern software engineering practices, leveraging AI coding assistants for enhanced productivity and mentoring peers.
Required Experience \& Qualifications
- 5\+ years of professional full\-stack development experience building complex, large\-scale distributed systems. Familiarity with Java, Python and JavaScript/Typescript based development.
- Extensive hands\-on experience with cloud\-native platforms such as AWS (preferred), GCP, or Azure.
- Proficiency in Kubernetes\-based orchestration, deployment, and operational troubleshooting.
- Solid engineering background in distributed systems and state management (Kafka, Redis, OpenSearch, and various multi\-modal databases).
- Deep expertise in frontend development using React.js, with a proven track record of delivering complex UIs (e.g., data\-heavy dashboards, mapping, and geo\-location products).
Must Have
- Verifiable experience building and deploying Deep Learning and Generative AI solutions (e.g., LLM\-based UIs, voice/image/video analytics using open\-source models).
- Practical experience incorporating AI\-assisted coding tools (Claude Code, GitHub Copilot/Codex, Devin) into your daily engineering workflow.
- Strong understanding of enterprise security concepts, specifically OAuth and AD integration.
Nice to Have
- Hands\-on DevOps, CI/CD, and infrastructure\-as\-code deployment experience, specifically within AWS.
- Additional background in large\-scale big data platform development, data crawling and data engineering/processing.
- Previous experience in lawful intercept, telecommunications data (5G, IMSI/IMEI tracking), cybersecurity analytics, or digital forensics.
- Proficiency with Golang and Rust.
Compensation \& Benefits
The expected base salary range for this position is $110,000 \- $135,000\. Actual compensation will be determined based on the candidate's skills, experience, and qualifications. This role is also eligible to participate in SS8's corporate bonus program, subject to the terms of the applicable plan. We offer a comprehensive benefits package including medical, dental, vision, 401(k) with company match, and paid time off.
SS8 is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender identity or expression, sexual orientation, age, disability, protected veteran status, or any other status protected by applicable law.
SS8 does not accept unsolicited resumes from staffing agencies, search firms, or third parties. Any resumes submitted without a signed agreement in place will be considered the property of Company, and no fees will be paid if a candidate is hired as a result.
Salary Context
This $110K-$135K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At SS8, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($122K) sits 32% below the category median. Disclosed range: $110K to $135K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
SS8 AI Hiring
SS8 has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Milpitas, CA, US. Compensation range: $135K - $135K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
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