Interested in this AI/ML Engineer role at Saronic Technologies?
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Saronic Technologies is a leader in revolutionizing autonomy at sea, dedicated to developing state\-of\-the\-art solutions that enhance maritime operations through autonomous and intelligent platforms.
Security at Saronic is a force multiplier, not a blocker. AI is being adopted fast across our company, and we’re looking for a Security Engineer for AI Platform Engineering to make AI both safe and self\-service. Think of this as a platform and enablement function for AI: you’ll build the paved, secure road so teams don’t have to take the shadow one. You’ll help e peoplacross the business use AI well, put the right guardrails and visibility in place, and build the more complex, well\-hosted, secure AI solutions that departments need so great ideas get built properly instead of turning into ungoverned risk and liability.
This is a customer\-facing role, and your customers are your colleagues in every department. You’ll partner with teams across the company to understand what they’re trying to accomplish, teach them to use AI effectively and safely, and build the solutions that need real engineering, security guardrails, and proper hosting.
This is an opportunity to define how an entire company uses AI safely, and own AI governance and security from the ground up, and build AI applications that make every department more capable.
How we think about building with AI. Anyone can make a demo now. A good\-looking front
end is nearly free, AI will generate a slick dashboard from a one\-line prompt, and it will look
impressive in a meeting. That is the easy part. The real skill, and what this role is about, is
using AI to build robust backends, infrastructure, and integrations, wired to real data and
real systems, that reliably solve a business problem in production. We hire people who can
tell the difference between something that looks like it works and something that works, and
who are drawn to the second.
What You’ll Do
- Enable Departments and Build: Build AI\-powered applications, agents, and automations for
teams across the company on our cloud platforms, with real backends, infrastructure, and
integrations to real data and systems, for properly hardened, compliant, well\-hosted, secure\-by\-default solutions, so departments don’t ship insecure ad\-hoc vibe\-coded tools themselves.
- Educate \& Set Standards: Teach teams to use AI safely and effectively for their own work,
and set company\-wide standards for good, safe AI usage.
- Govern: Own AI governance, visibility, and inventory; monitoring and logging of AI usage;
and prompt\- and output\-level data\-loss\-prevention to protect sensitive data, including
customer data.
- Guardrails: Put guardrails in place for AI usage, treat AI agents as identities with least
privilege, govern model and agent access, and make the sanctioned path the best path so
“shadow AI” doesn’t take hold.
- Craft Reliable AI: Build agents and workflows that actually work, design tool use and MCP
integrations, manage context and memory, and validate quality with evaluative loops.
How You Work
- How you work matters as much as what you build. This role represents our team to the
entire company, so we’re looking for someone who is genuinely energized by teaching and
unblocking people, not someone who wants to be the smartest person in the room. You’ll
thrive here if you are:
- Kind and patient by default. You meet people where they are, answer the “basic”
question as generously as the hard one, and never make someone feel small for not
knowing something. You stay with people through the problem instead of handing off
a fix and making it someone else’s problem.
- Low\-ego and intellectually humble. You explain technical ideas to non\-technical
colleagues without condescension, you say “I don’t know, let’s find out,” and you own
your mistakes. Strong opinions, loosely held.
- A natural teacher and enabler. You get more satisfaction from making ten
colleagues more capable than from being the lone hero, and you turn one\-off help into
reusable guidance and patterns.
- Deeply empathetic and a good listener. You start from the person’s actual problem,
not your preferred solution, and you’re the voice of the user back to the security and
platform teams.
- Values\-aligned and high\-agency. You collaborate across engineering, security, legal,
and business teams with integrity, and you navigate ambiguity while keeping
everyone with you.
Required Qualifications
- You’re a genuine AI power user who builds: you’ve shipped agents, tools, or automations with LLMs, and you can walk through the hard parts
- You build real backends and infrastructure, not just demos: APIs, data pipelines, authentication and secrets, hosting and deployment (containers, Infrastructure\-as\-
- Code, CI/CD), and systems integration, made reliable with observability, evals, and
graceful failure handling
- Fluency with modern agent concepts: agentic loops, agent harnesses, context
engineering, tool use / function calling, MCP, and evals
- Working knowledge of the strengths and weaknesses of different AI models and
platforms, and how to match the right model to a task
- Enough software and security foundation to build and secure production systems,
with sound judgment on data handling and safe AI usage
- Immense kindness and patience for supporting and teaching non\-technical users
- Ability to obtain and maintain a U.S. security clearance
- We are not looking for degrees or certifications in Artificial Intelligence. We care about what
you’ve built and what you can do. Self\-taught practitioners and career\-changers are
welcome; this role is about outcomes and attitudes.
Preferred Qualifications
- Experience with AI governance, DLP, monitoring/logging, or guardrails for AI usage
- Building and hosting AI solutions in the cloud
- A security background (application, cloud, or data protection)
- Experience teaching, enabling, or supporting non\-technical teams
*If this role is based in the United States, it requires access to export\-controlled information or items that require “U.S. Person” status. As defined by U.S. law, individuals who are any one of the following are considered to be a “U.S. Person”: (1\) U.S. citizens, (2\) legal permanent residents (a.k.a. green card holders), and (3\) certain protected classes of asylees and refugees, as defined in* *8 U.S.C. 1324b(a)(3\)**.*
Saronic does not discriminate on the basis of race, sex, color, religion, age, national origin, marital status, disability, veteran status, genetic information, sexual orientation, gender identity or any other reason prohibited by law in provision of employment opportunities and benefits. *We are also committed to providing reasonable accommodations for qualified individuals with disabilities.*
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Saronic Technologies, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Saronic Technologies AI Hiring
Saronic Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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