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About This Role
Are you looking to Optimize your life? Start your exciting path to a rewarding career today!
We are Optimum, a leader in the fast-paced world of connectivity, and we're seeking driven and enthusiastic professionals to join our team, empower lives, fuel businesses, and drive innovation. Connectivity is now longer a luxury, but a necessity. A career at Optimum means you'll be enabling progress and enhancing lives by providing reliable, high-speed connectivity solutions that keep the world connected. Our successes, now and in the future, are powered by our amazing product, a commitment to our people and culture, and the connections we make in our communities.
If you are resourceful, collaborative, and passionate about delivering consistent excellence, Optimum is for you!
Job Summary
As a Senior SOC Engineer (AI & Automation), you will design, build, and operate the AI, automation, and detection-engineering capabilities that power our Security Operations Center. Bridging security operations and software engineering, you will develop AI-driven detection, triage, and response tooling, integrate large language model (LLM) and agentic workflows into analyst operations, and ensure those capabilities are accurate, safe, measurable, and continuously improved. As a senior member of the team, you will also serve as a technical leader during major security incidents, leading investigations and turning lessons learned into stronger detections, automations, and playbooks.
Responsibilities
- Design, build, and maintain AI/ML- and automation-driven capabilities for alert enrichment, correlation, summarization, triage, and prioritization.
- Develop and maintain SOAR automations and detection-as-code pipelines that are version-controlled, tested, and peer-reviewed.
- Integrate LLM and agentic AI tooling into SOC workflows (copilots, auto-triage agents); engineer prompts, guardrails, and evaluation harnesses.
- Evaluate, benchmark, and tune AI models and tools for security use cases, measuring precision and recall, false-positive reduction, and impact on mean time to detect and respond (MTTD/MTTR).
- Build data pipelines and feature engineering from security telemetry to support detection and machine-learning use cases.
- Apply MLOps practices, including model versioning, monitoring, drift detection, and retraining, to security models running in production.
- Ensure responsible and secure AI use, including data governance, prompt-injection and model-abuse defenses, privacy, and output validation.
- Partner with detection engineers, SOC analysts, and incident responders to operationalize tooling and feed lessons learned back into models and automations.
- Serve as a senior escalation point and incident commander for complex and major incidents, coordinating cross-functional response and directing technical workstreams.
- Lead investigations and forensic analysis for escalated incidents and provide hands-on incident response support across on-premises and cloud environments.
- Own post-incident reviews and root cause analyses, translating lessons learned into new detections, automations, and playbook improvements.
- Develop, run, and mature incident-response playbooks and tabletop exercises (TTX) to validate and improve organizational readiness.
- Define and report detection and incident-response metrics (e.g., MTTD, MTTR) to measure and continuously improve SOC effectiveness.
- Mentor analysts and engineers, fostering a culture of continuous learning across AI-augmented and incident-response workflows, and promote an AI-first operating model across the SOC.
Qualifications
- Bachelor's degree in Computer Science, Engineering, or related field, or equivalent years of experience
- 7+ years of combined experience across security operations, incident response, and software engineering
- Hands-on incident response and digital forensics experience, including leading or coordinating response to complex and major incidents preffered
- Strong programming skills (e.g., Python) and sound software-engineering practices, including version control, CI/CD, and automated testing
- Hands-on experience building with AI/ML, including large language models, prompt engineering, retrieval-augmented generation (RAG), and/or agentic frameworks
- Experience with SOAR/automation and detection engineering (detection-as-code)
- Data engineering skills, including working with large security datasets, APIs, and pipelines
- Working knowledge of SOC operations and the incident lifecycle, including the MITRE ATT&CK framework, the NIST incident response lifecycle (NIST SP 800-61), the Cyber Kill Chain, and SANS PICERL
- Cloud security and cloud-platform experience
- Awareness of AI and LLM security risks, such as prompt injection and the OWASP LLM Top 10
- Ability to translate fluently between security and engineering stakeholders.
Preferred Qualifications
- MLOps experience deploying and maintaining models in production
- Relevant security and/or AI/ML certifications, including incident-response and forensics credentials (e.g., GCIH, GCFA, GCFE, GNFA) or CISSP/CISM
At Optimum, every action and interaction we take part in, is driven by our three Guiding Principles: Do What’s Right, Drive One Optimum, and Make It Happen. These aren’t just words, they help us build trust, create real community, and embrace new ways of thinking. Our employees are empowered to do the right thing for our customers and co-workers and to recognize and reward these behaviors when we see them. It’s all part of the bigger picture of “Be The Difference” where each employee knows they have the power to enact real change, share new ideas, and understand that learning never stops.
If you have the drive to succeed and are ready to embark on a thrilling career, seize this opportunity today, and join our winning team. Together, we'll shape the future of connectivity.
All job descriptions and required skills, qualifications and responsibilities for a particular position are subject to modification by the Company from time to time, in the Company’s discretion based on business necessity.
We are an Equal Opportunity Employer committed to recruiting, hiring and promoting qualified people of all backgrounds regardless of gender, race, color, creed, national origin, religion, age, marital status, pregnancy, physical or mental disability, sexual orientation, gender identity, military or veteran status, or any other basis protected by federal, state, or local law.
The Company collects personal information about its applicants for employment that may include personal identifiers, professional or employment related information, photos, education information and/or protected classifications under federal and state law. This information is collected for employment purposes, including identification, work authorization, FCRA-compliant background screening, human resource administration and compliance with federal, state and local law.
Applicants for employment with The Company will never be asked to provide money (even if reimbursable) as part of the job application or hiring process. Please review our Fraud FAQ for further details.
Pay is competitive and based on a number of job-related factors, including skills and experience. The starting pay rate/range at time of hire for this position in the posted location is $100,246.00 - $164,689.00 / year. The rate/range provided herein is the anticipated pay at the time of hire, and does not reflect future job opportunity.
We appreciate your interest in this opportunity. Applicants must be authorized to work for ANY employer in the U.S. Please note that at this time, we do not provide visa sponsorship for employment.
Salary Context
This $100K-$164K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Optimum, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($132K) sits 38% below the category median. Disclosed range: $100K to $164K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Optimum AI Hiring
Optimum has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Norwalk, CT, US, New York, NY. Compensation range: $164K - $164K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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