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About This Role
Why NinjaTech AI
We are building the unmetered intelligence workforce: autonomous AI agents that do real, end\-to\-end work for enterprises instead of just answering questions. Since founding in 2022 we have gone from research to agents running production workloads inside real companies, on secure, isolated infrastructure, backed by SRI International, DCVC, and Candou Ventures. This is a small, senior team moving at startup speed on one of the most consequential problems in technology, and the Applied AI Engineer sits at the sharpest edge of it: your work lands in front of real customers in weeks, not quarters, and directly shapes how the world's most demanding enterprises adopt agentic AI. If you want the shortest possible line between what you build and the outcomes it creates, this is a rare seat.
Why this role exists
When we close a large enterprise deal, the contract is the start of the hard part, not the end. The customer has bought an outcome, an "unmetered intelligence workforce" doing real work inside their business, and someone has to make that outcome real, fast, inside their systems, their security constraints, and their messy data. That someone is the Applied AI Engineer. We attach an Applied AI Engineer to every large enterprise account. They embed with the customer, build the first high\-value agentic workflows on our platform, and own technical success from kickoff through go\-live and expansion. The Applied AI Engineer is how a signed contract becomes a renewed and expanded one.
What you'll own
- Land the deployment. Embed with a newly\-closed enterprise customer, rapidly learn their operations and data, and stand up the first production agent workflows on NinjaTech in weeks, not quarters.
- Build custom, tailored solutions. Wire our platform into the customer's real systems (their data sources, identity, ticketing, CRMs, internal APIs) and build the bespoke automations, prompts, tools, and guardrails that make agents useful for their jobs\-to\-be\-done.
- Be the technical face of NinjaTech in the room. Run working sessions with everyone from the customer's frontline operators to their CISO and CTO. Translate their vision into an architecture, and translate our platform into their language.
- Drive adoption and expansion. A deployment that isn't used doesn't renew. Instrument usage, hunt for the next high\-value workflow, and turn a beachhead into an org\-wide rollout. Your success metric is customer outcomes that drive net revenue retention.
- Clear enterprise gauntlets. Own the technical side of security review, isolated\-VM/data\-residency requirements, SSO/SCIM, SOC 2 / DPA questionnaires,and procurement's technical due diligence.
- Close the loop to Product. You see what breaks in the field first. Feed sharp, prioritized signal back to core engineering; occasionally upstream a fix or a reusable component so the next deployment is faster.
What we require
- Engineering background. You're a genuine builder. Proficient in Python and at least one of TypeScript/JavaScript, Java, or Go; comfortable across data plumbing, APIs, and a bit of front\-end when a demo needs it.
- Genuine curiosity about agentic AI and LLM systems: prompt/tool design, evals, orchestration, guardrails, and the failure modes of agents in production. You don't need to have built them at scale, but you should be hungry to.
- Data fluency. You can wrangle messy, large\-scale, real\-world data and reason about storage, pipelines, and cloud infrastructure.
- Executive presence and operator empathy. You can hold a credible technical conversation with a CTO and sit with a frontline user to understand their actual workflow. You listen before you build.
- Bias to ship. You move with speed and precision, iterate with users, and are energized (not frustrated) by evolving objectives.
- Willingness to travel up to \~25% to customer sites, and to be onsite in the Bay Area with the core team.
What we value (nice\-to\-haves)
- Experience integrating into regulated or security\-sensitive enterprise environments (isolated VMs, on\-prem/VPC, SOC 2, HIPAA, FedRAMP\-adjacent).
- Prior forward\-deployed / solutions\-engineering / delivery experience at an enterprise\-software or infra company.
- A track record of turning a first deployment into a much larger footprint.
Compensation \& logistics
- Base range $80K to $120K depending on level and experience, plus meaningful equity and a delivery/expansion\-linked bonus.
- Onsite in the Bay Area with the core team; \~25% travel to customer sites.
- Full benefits (medical / dental / vision, etc.).
Equal opportunity
NinjaTech AI is an equal opportunity employer. We celebrate diversity and are committed to building an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, or any other characteristic protected by law. All qualified applicants will receive consideration for employment.
Salary Context
This $80K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At NinjaTech AI, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $80K to $120K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
NinjaTech AI AI Hiring
NinjaTech AI has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Mountain View, CA, US. Compensation range: $120K - $120K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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