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About This Role
Nexxais building the best AI systems 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 Backend AI Engineers to design, build, and own the core AI infrastructure and services that power Nexxa's products in production. Where our Forward Deployed Engineers embed with customers to deliver solutions on the ground, this role builds the systems that make those solutions possible at scale: model-serving pipelines, inference and orchestration layers, data pipelines, and the APIs and microservices that connect Generative AI, Computer Vision, and Machine Learning models to real enterprise environments.
This role is a blend of backend software engineering, ML infrastructure, and systems architecture. You'll design distributed systems, integrate and serve models in production, and build the reusable platform capabilities that our customer-facing and product teams depend on.
Key Responsibilities
- Design, build, and maintain backend services and APIs that power GenAI, LLM, and Computer Vision model integrations across Nexxa's products.
- Build and own core AI/ML infrastructure: model-serving pipelines, inference services, data pipelines, and embedding/vector stores.
- Architect scalable, production-grade systems for real-time and batch AI workloads across manufacturing, infrastructure, and logistics domains.
- Implement and optimize RAG systems, prompt/context pipelines, and orchestration layers connecting models to enterprise and operational data sources.
- Build robust APIs, microservices, and integration layers connecting AI systems to customer data, legacy systems, and existing infrastructure.
- Own the reliability, performance, and observability of backend AI systems — logging, monitoring, testing, and CI/CD for ML services.
- Collaborate closely with Forward Deployed Engineers, ML engineers, and product teams to translate customer and field requirements into reusable, hardened backend capabilities.
- Evaluate and integrate ML/CV/LLM models into production backend systems; manage model versioning, rollout, and deployment pipelines.
- Produce clear technical documentation: architecture diagrams, API specs, and runbooks for internal and customer-facing teams.
- Mentor engineers and contribute to internal backend engineering best practices.
Qualifications
- 4–8+ years of experience in backend software engineering, ML/platform engineering, or similar roles.
- Strong proficiency in TypeScript/Node.js (our primary backend language), with strong API and microservice design skills. Working proficiency in Python is a plus for ML/model integration work.
- Hands-on experience building and operating production backend systems at scale — distributed systems, databases, message queues.
- Experience integrating ML or Generative AI models (LLMs, multimodal models) into backend services — inference, orchestration, and evaluation.
- Solid understanding of cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Experience designing and operating data pipelines (batch and/or streaming) across structured and unstructured data.
- Hands-on experience building retrieval-augmented generation (RAG) systems and AI memory architectures — retrieval pipelines, vector stores, context management, and long-term/session memory for LLM applications.
- Strong grasp of system design fundamentals: scalability, reliability, security, and observability.
- Comfortable working cross-functionally with ML engineers, product, and customer-facing teams.
- Bachelor's degree (or higher) in Computer Science or a related field.
Preferred
- Familiarity with ML frameworks (PyTorch, TensorFlow, OpenCV) sufficient to integrate, serve, or evaluate models, even without training them yourself.
- Experience with MLOps tooling: model registries, feature stores, CI/CD for ML, and monitoring/observability for ML systems.
- Background in event-driven or real-time systems (Kafka, gRPC, WebSockets).
- Experience in industrial, IoT, or operational technology (OT) environments.
- Experience in startup or high-growth environments.
What We're Looking For
- A backend engineer who wants to build the infrastructure powering real-world autonomous AI systems.
- Someone who can architect for scale and reliability while still moving fast.
- A systems thinker who enjoys turning ambiguous AI capabilities into dependable, production-grade backend services.
- A strong collaborator who partners well with ML engineers, Forward Deployed teams, and product.
Why Join Nexxa.AI?
Innovative Environment: Build the foundational systems behind groundbreaking AI and automation technologies transforming heavy industries.
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 backend engineering and eager to build 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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