Interested in this AI/ML Engineer role at SORONA?
Apply Now →About This Role
Rosenxt is a forward\-thinking technology group — we are visionary architects of progress with 45 years of engineering excellence.
As a privately owned global partner, we look far beyond tomorrow, are committed to the long\-term and thus turning opportunities into successful ventures. We are tech enthusiasts through and through, diving deep into the latest technologies. This expertise in various technology fields, such as sensors, autonomous robotic, AI or advanced materials and our strong R\&D mindset allows us to develop highly innovative products and services for customer in most challenging environments such as subsea, industrial, renewables, or the integrity of water and energy supply. Our purpose goes beyond pure business; it's about creating progress and sustainable value — for our customers, our partners, and society at large.
Why work for Rosenxt?
At Rosenxt, you'll work on technology that pushes the boundaries of sensing, embedded systems, and intelligent data acquisition for some of the world's most demanding environments.
Our engineering teams collaborate across the United States, working across software, hardware, data science, and machine learning to develop next\-generation sensing and inspection solutions. From embedded sensor development and FPGA integration to large\-scale data analytics and AI\-driven insights, our teams combine deep technical expertise across the entire technology stack. Hardware\-focused development takes place primarily in San Luis Obispo, California, our main engineering and prototyping hub in the U.S. Our software engineers work remotely across multiple locations and time zones, enabling us to attract top talent nationwide while staying closely connected through regular in\-person collaboration in Columbus, Ohio. You'll take ownership of meaningful engineering challenges, collaborate with experienced engineers around the world, and contribute from concept and prototyping through testing and validation.
If you're passionate about innovation, advanced technology, and creating solutions with real\-world impact, Rosenxt offers an environment where you can grow your skills and help shape what's next.
Rosenxt USA seeks to add a:
Workgroup Lead \- Software and AILocation:
Columbus
This position is fully remote within the US East Coast, with preference for candidates located in or near Columbus, Ohio, and close collaboration with our supervisory organization in Germany as well as our US legal entity management in California.
We are a technology\-driven company with several innovative business units in the growth phase. While we maintain certain centralized structures, we foster an agile, start\-up\-like spirit. Our teams develop advanced AI and software solutions that process large sensor datasets, build intelligent cloud services, and power real\-time edge applications. We operate in domains where security, reliability, and engineering excellence are essential.
In this international environment, you will lead a multidisciplinary engineering team working mostly remotely across different US regions and Europe.
Summary
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We are looking for a Workgroup Lead to be the on\-the\-ground engineering leader for our US\-based team. You will have line management responsibility for a cross\-disciplinary team of approximately \[12?] engineers spanning AI/ML, cloud backend, frontend, and DevOps. You will serve as the US leadership anchor within a distributed organization that operates across US, Europe, and Southeast Asia.
This role reports to the Head of Software and AI, with additional task management from the Head of AI, based in Europe, and partners closely with US branch management in California. It is designed for someone who thrives with a high degree of autonomy and wants to shape how a growing team operates.
Responsibilities
### People Leadership
- Own line management for a co\-located team of AI/ML engineers, software engineers, and DevOps engineers across the US.
- Hold regular 1:1s, set development goals, conduct performance reviews, and support career growth for each team member.
- Build a cohesive, high\-trust team culture in a distributed environment where colleagues span multiple US regions and Europe.
- Encourage transparency, ownership, and continuous learning within the team.
### Delivery
- Support the team’s delivery cadence, ensuring work is planned, blockers are removed early, and commitments are met.
- Drive engineering quality standards: CI/CD pipelines, code review practices, cloud automation, monitoring, and documentation.
- Identify process gaps and implement improvements that help the team deliver more effectively.
### Cross\-organization collaboration
- Build a strong team culture and improve collaboration between engineers who work across different US regions and Europe. Act as the primary link between the US engineering team and the supervisory AI organization in Germany.
- Collaborate with US branch management in California on HR, operational, and organizational processes.
### Technical Engagement
- Participate meaningfully in technical discussions around AI/ML pipelines, cloud infrastructure, and system architecture. You don’t need to be the deepest specialist, but you need to engage credibly.
- Help the team make sound architectural decisions and maintain alignment with the broader technology strategy.
Qualifications
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### Essential skills
- 7\+ years in software engineering, with at least 3 years in a people leadership or team management role.
- Demonstrated experience managing cross\-disciplinary engineering teams (not solely AI/ML or solely traditional software).
- Strong people skills: coaching, motivation, constructive feedback, performance management, and handling difficult conversations.
- Proven ability to build team cohesion and maintain high morale in distributed or fully remote settings.
- Depth in at least one of: AI/ML systems, cloud\-native backend services, or DevOps, with working fluency across the others.
- Excellent communication skills, particularly across time zones, cultures, and organizational boundaries.
- B.S. in Computer Science, AI/ML, Software Engineering, or a related technical field.
### Desirable skills
- M.S. or higher in a relevant discipline.
- Experience with AI/ML systems in production environments (beyond experimentation or proof\-of\-concept).
- Background in industrial, safety\-critical, or regulated environments (oil \& gas, subsea, energy, defense, or similar).
- Familiarity with regulatory frameworks such as the EU AI Act or equivalent compliance standards.
- Experience working within or reporting to a European parent organization.
- Entrepreneurial mindset \- comfortable operating with ambiguity, taking initiative, and building processes from the ground up.
### Soft skills
- Communication: Communicate effectively across organizational levels, cultures, and time zones
- People development: Actively coach and develop team members across different technical disciplines, providing constructive feedback, identifying growth paths, and having difficult conversations when needed
- Conflict resolution and trust\-building: Build cohesion within a cross\-disciplinary team, navigate interpersonal friction constructively, and foster psychological safety
- Influence: Build relationships and drive alignment through persuasion and credibility
- Adaptability: Guide a team through organizational change, evolving scope, and ambiguity as the organization scales, adjusting approaches when needed
- Prioritization and delegation: Manage the team's workload effectively, make trade\-off decisions, shield the team from unnecessary noise, and know when to delegate versus when to step in.
- Curiosity and continuous learning: Stay current with developments in AI/ML and engineering practices, but also with evolving approaches to engineering management and team health
- Organizational awareness: Understand how decisions impact across projects and geographies, read the broader business landscape, and know when to escalate, when to absorb, and when to advocate.
- Purpose\-driven leadership: Connect the team's daily work to the broader mission(s) of the organization, keeping people motivated and engaged
- Self\-starter: Operate proactively with a high degree of autonomy, identifying opportunities and problems before they surface, and building processes and structures where none yet exist.
Our Offer:
Rosenxt offers an exceptional working environment, salary commensurate with experience and incredible benefits package.
Benefits include:
- 401(k) matching up to 5%, immediately vested
- Generous health benefits, effective immediately
- Medical (PPO, HSA), Dental, Vision, Flexible Spending Accounts
- Flexible work schedule (Friday half\-days off)
- Incredible work\-life balance and flexibility
- Immediate Vacation time available, Holidays, Paid Time Off
- Travel to Europe to work with Rosenxt colleagues
- Employee assistance program
- Life insurance
- Parental leave
- Professional development assistance
- Referral program
- Relocation assistance
- Tuition reimbursement
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 SORONA, 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. Senior-level AI roles across all categories have a median of $230,000.
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.
SORONA AI Hiring
SORONA has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Columbus, OH, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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