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About This Role
What We Do:
Cybercrime is growing, and more businesses are getting hit by threats that used to target only the biggest organizations. That pushes defenders like us to operate at the highest level, and it deepens our need for good people who want to make a meaningful impact.
Founded in 2015 by former NSA cyber operators, Huntress is a remote-first team working to make enterprise-grade cybersecurity accessible to businesses of all sizes. We work closely with security teams and service providers protecting complex environments, often without the time or headcount to handle it all. That's why we build our technology in-house and back it with a 24/7 human-led Security Operations Center (SOC). As a result, our platform is never disconnected from the experts who manage it, ensuring our customers' protection.
Huntress now secures more than 6M endpoints and 16M identities worldwide. Those numbers keep growing because more businesses rely on us to help carry the load and operate with more confidence. Every day, you can see that commitment in how we stand with our customers and how we show up for each other.
Reports to: Director, Engineering
Location: Remote US, Only
Compensation: $215,000 to $240,000 base plus bonus and equity
What You'll Do:
Huntress is building an Agentic SOC. When our detection pipeline raises a signal, an LLM-based agent picks it up and investigates it end-to-end. It gathers telemetry across our products, reconstructs what happened, and makes a well-documented determination about whether the activity is malicious. Over time, it will take on more of the work itself, growing from resolving routine cases to acting fast when it finds an attack and handing analysts cases with the evidence already assembled. We're hiring a Principal Software Engineer to head this effort.
This system is meant to augment our analysts, not replace them. It takes the high-volume, repetitive work off their plates so they can focus on complex cases and hands-on keyboard intrusions where human expertise matters most. It also reduces toil in the rest of their day by using LLMs to pre-process the output of the tools analysts use constantly, so they start from a clear picture instead of raw telemetry.
This isn't a chatbot bolted onto a dashboard. The system investigates a large volume of signals a day across thousands of customer organizations. Many signals are benign, but some are live intrusions where minutes matter. The telemetry the system reads comes from environments in which attackers are actively operating. Because the system makes real decisions, like closing an investigation or isolating a host, it has to be right, it has to show its work, and it has to know when to hand off to a person.
As the technical lead, you'll own the system's architecture and direction. That means deciding how an investigation is structured, where deterministic code ends and model reasoning begins, what the agent can and can't touch, how we know it's working, and which decisions it makes on its own as it proves itself. You'll work closely with SOC analysts, detection engineers, product, and other engineering teams. You'll go deep in the code and help the engineers around you grow. Your work will directly shape how Huntress scales protection for the 99%.
We make full use of AI coding tools to enable you to accelerate your development. We don't cap token usage. We expect you to use AI and all tools responsibly.
Responsibilities:
- Own the architecture of agentic investigation. You help define how signals move from detection into investigation, how investigation state is managed, how tools are designed and exposed to agents, and what a finished investigation contains: the verdict, the timeline, the scope, and the evidence behind every claim. When the team is stuck between options, you make the call, and the foundations you set are what others build on for years.
- Build agents that investigate like a strong analyst. You work alongside our SOC and product researchers to learn how experienced analysts actually triage and investigate, then turn that into agent behavior: which questions to ask, which evidence to gather, when to dig deeper, and when to stop and hand off to a human.
- Take toil off analysts' plates. Beyond full investigations, you build LLM-powered pre-processing for the tools and data sources analysts rely on every day. It summarizes, correlates, and highlights what matters, so analysts spend their time on judgment instead of assembly.
- Bring a security mindset to the agent itself. The agent reads attacker-influenced data, runs across thousands of customer tenants, and can take action in customer environments. You design as if someone is trying to manipulate it, because someone will. Trust boundaries, tenant isolation, and auditability are part of the architecture from the start.
- Treat evaluation as a core part of the product. You define how we measure whether an investigation is correct, well-reasoned, and well-documented, and you make sure no prompt, model, or tool changes without evidence that it helps.
- Decide with data what the system does on its own. Based on measured performance, you decide where it acts autonomously, where a person reviews its work, and how that changes over time. You also recognize when a problem doesn't need an agent at all.
- Build for production, not for demos. You design for reliability, latency, and cost at scale. The system keeps working when models are slow, wrong, unavailable, or quietly changed underneath you, and anyone can trace exactly why an investigation reached its conclusion or why an action was taken.
- Augment analysts, don't replace them. You design the handoff between agent and analyst so that when a case reaches a person, the agent's work makes them faster and better informed. You consider how analysts' work changes as agents take on more of it, and how we explain agent decisions to partners and customers in a way they can trust.
- Prototype your way through hard problems. When the team hits something genuinely uncertain, you dig in first: derisk it, prove out the approach, then hand it off clearly.
- Be the engineering face of the Agentic SOC. In conversations with SOC leadership, other engineering teams, product, and executives, you represent the team's technical perspective clearly and credibly. You drive alignment on key decisions, carry context in both directions, and protect the team from unclear scope.
- Balance technical rigor with pragmatism. You push back when a shortcut creates real risk, and you also push back on over-engineering when the goal is discovery. You have a bias for action and frequent feedback from analysts and customers, and you can explain your tradeoffs to engineers and non-engineers alike.
- Raise the floor and the ceiling. Your technical leadership is measured by how the whole team performs, not just by what you personally ship. That shows up in thoughtful code review, pairing on problems others are stuck on, and creating leverage through better patterns, tooling, and shared understanding. You partner with the engineering manager on team health, give direct feedback, and model the judgment and accountability you want the team to have.
What You Bring To The Team:
- 15+ years of experience developing complex software products, including significant time as the technical lead on production systems
- Hands-on experience designing, shipping, and operating LLM-based agents in production, covering tool use, context management, structured output, guardrails, and evaluation, along with a clear-eyed understanding of how they fail
- A track record of building evaluation for non-deterministic systems, such as labeled datasets, offline evals, and regression gates, and of measuring quality over time rather than at launch
- A security mindset: experience building systems that handle untrusted input, enforce multi-tenant isolation, and produce an audit trail that holds up under scrutiny
- Experience building systems that make automated decisions with real consequences, and the judgment to know what to automate and what to leave to people
- Curiosity about how security analysts think and work, and a willingness to spend real time learning from them
- Deep backend expertise in distributed systems, queues, and durable workflows, and concurrency, with a track record of designing for throughput and correctness under load
- Strong skills in one or more backend languages and the ability to pick up new ones quickly. Our primary stack is Ruby on Rails, and we expect you to become fluent in it
- Experience with AWS, Azure, or other public cloud environments, and with data stores such as Postgres and Redis
- Experience with AI coding tools, such as Claude Code
- BS or MS in Computer Science or Engineering, or equivalent experience
Bonus Points:
- Experience working in or building for a SOC, incident response, threat hunting, or detection engineering. It isn't required, but it helps a lot, because we build these workflows around how our analysts actually investigate
- Familiarity with endpoint and identity telemetry and with attacker tradecraft
- Experience attacking or adversarially testing ML or LLM systems
- Experience with Ruby on Rails
What We Offer:
- 100% remote work environment - since our founding in 2015
- Generous paid time off policy, including vacation, sick time, and paid holidays
- 12 weeks of paid parental leave
- Highly competitive and comprehensive medical, dental, and vision benefits plans
- 401(k) with a 5% contribution regardless of employee contribution
- Life and Disability insurance plans
- Stock options for all full-time employees
- One-time $500 reimbursement for building/upgrading home office
- Annual allowance for education and professional development assistance
- $75 USD/month digital reimbursement
- Access to the BetterUp platform for coaching, personal, and professional growth
Huntress is committed to creating a culture of inclusivity where every single member of our team is valued, has a voice, and is empowered to come to work every day just as they are.
We do not discriminate based on race, ethnicity, color, ancestry, national origin, religion, sex, sexual orientation, gender identity, disability, veteran status, genetic information, marital status, or any other legally protected status.
We do discriminate against hackers who try to exploit businesses of all sizes.
Accommodations:
If you require reasonable accommodation to complete this application, interview, or pre-employment testing or participate in the employee selection process, please direct your inquiries to [email protected]*. Please note that non-accommodation requests to this inbox will not receive a response.*
Huntress uses artificial intelligence tools to assist in reviewing and evaluating job applications, including resume screening, skills assessment, and candidate matching and comparisons. These AI tools support our human recruiters in the initial review process, but do not make final hiring decisions without human involvement. By submitting your application, you acknowledge this use of AI in our recruitment process. Please review our Candidate Privacy Notice for more details on our practices and your data privacy rights.
#BI-Remote
Salary Context
This $215K-$240K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Huntress Labs, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($227K) sits 7% above the category median. Disclosed range: $215K to $240K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Huntress Labs AI Hiring
Huntress Labs has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $190K - $240K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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