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About This Role
Nexxa.ai is building artificial super intelligence for heavy industries — enabling machines, systems and operations to think, decide and act autonomously across manufacturing, large-scale infrastructure, logistics and legacy environments.
Our mission is to translate deep technical breakthroughs into operational reality, solving some of the hardest systems-level problems in industry.
Role Overview
We’re looking for Applied AI Engineers to work as forward deployed directly with customers and lead the end-to-end delivery of high-impact technical solutions. You’ll embed deeply with customer teams, translating real-world challenges into production-ready systems that leverage Generative AI, Computer Vision, and Machine Learning.
This role is a blend of software engineering, ML engineering, architecture, and consulting. You’ll design and deploy solutions, integrate models, build custom workflows, and guide customers through successful implementation. This position requires frequent travel to customer sites.
Key Responsibilities
- Engage directly with enterprise and strategic customers to understand their workflows, data, and technical requirements.
- Architect, build, and deploy custom solutions leveraging GenAI, LLMs, Machine Learning and Vision models, and customer data sources.
- Lead full project lifecycles: scoping, solution design, development, implementation, testing, deployment, and iteration.
- Integrate and optimize AI/ML pipelines, including data preprocessing, prompt engineering, model selection, and evaluation.
- Build reliable, scalable software integrations using APIs, cloud services, and containerized systems.
- Troubleshoot complex technical issues across the stack—applications, models, data pipelines, infrastructure, and integrations.
- Act as the customer’s trusted technical advisor, enabling adoption of new product capabilities and AI features.
- Partner closely with internal product and engineering teams to communicate customer feedback and shape roadmap direction.
- Produce high-quality documentation, architecture diagrams, runbooks, and technical assets for customer teams.
- Mentor junior engineers and contribute to internal best practices for FDE delivery.
Qualifications
- 5–10+ years in engineering roles such as Forward Deployed Engineer, ML Engineer, Software Engineer, Solutions Engineer, Technical Consultant, or similar.
- Strong proficiency in Python, JavaScript/TypeScript, Go, or similar production-oriented languages.
- Hands-on experience with Machine Learning, including training, fine-tuning, evaluating, or deploying models.
- Direct experience with Generative AI (LLMs, multimodal models) and applying them to real-world problems.
- Exposure to Computer Vision techniques (detection, segmentation, OCR, embeddings, multimodal pipelines).
- Strong knowledge of ML frameworks (PyTorch, TensorFlow, OpenCV, etc.).
- Experience with cloud infrastructure (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
- Excellent communication skills with both technical and non-technical audiences.
- Comfort leading customer-facing engagements and guiding stakeholders through ambiguity.
- Willingness and ability to travel frequently.
Preferred
- Prior experience in consulting, technical solutions, professional services, or customer-embedded technical roles.
- Experience with vector databases, embedding pipelines, or retrieval-augmented generation (RAG).
- Experience building APIs, microservices, or distributed systems.
- Familiarity with MLOps tools (Docker, Kubernetes, model registries, CI/CD for ML).
- Background in deploying or fine-tuning CV models (YOLO, SAM, CLIP, DETR, etc.).
- Experience in startup or high-growth environments.
What We’re Looking For
- A customer-obsessed senior engineer who thrives in deeply technical, fast-moving environments.
- A creative problem solver who can translate vague requirements into robust, scalable solutions.
- Someone excited to combine software engineering with real-world AI deployments.
- A leader who can own outcomes end-to-end and influence both customer and internal teams.
Why Join Nexxa.AI?
+ Innovative Environment: Play a critical role in transforming heavy industries through groundbreaking AI and automation technologies. + Collaborative Culture: Be part of a team that values innovation, discipline, and continuous improvement. + Professional Growth: Benefit from significant opportunities for career development and advancement. + Competitive Compensation: Enjoy a comprehensive salary and equity package reflective of your expertise and contributions.If you're passionate about development and eager to shape the infrastructure powering advanced AI solutions, we'd love to connect.
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 NEXXA, 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. Mid-level AI roles across all categories have a median of $193,200.
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.
NEXXA AI Hiring
NEXXA has 4 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Sunnyvale, CA, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 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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