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About This Role
Job Title: Gen AI Engineer
Location: Remote
Employment Type: Full\-Time
About Us:
Amira Learning accelerates literacy outcomes by delivering the latest reading and neuroscience with AI. As the leader in third\-generation edtech, Amira listens to students read out loud, assesses mastery, helps teachers supplement instruction and delivers 1:1 tutoring. Validated by independent university and SEA efficacy research, Amira is the only AI literacy platform proven to achieve gains surpassing 1:1 human tutoring, consistently delivering effect sizes over 0\.4\.
Rooted in over thirty years of research, Amira is the first, foremost, and only proven Intelligent Assistant for teachers and AI Reading Tutor for students. The platform serves as a school district’s Intelligent Growth Engine, driving instructional coherence by unifying assessment, instruction, and tutoring around the chosen curriculum.
Unlike any other edtech tool, Amira continuously identifies each student’s skill gaps and collaborates with teachers to build lesson plans aligned with district curricula, pulling directly from the district’s high\-quality instructional materials. Teachers can finally differentiate instruction with evidence and ease, and students get the 1:1 practice they specifically need, whether they are excelling or working below grade level.
Trusted by more than 2,000 districts and working in partnership with twelve state education agencies, Amira is helping 3\.5 million students worldwide become motivated and masterful readers. Job Summary:
A self\-motivated A\-player who is results\-oriented, operates at a fast pace, and takes pride in delivering high\-quality, trustworthy AI systems; an experienced generative AI practitioner with deep expertise in LLM\-based system design, including prompt engineering, RAG architectures, fine\-tuning, and evaluation; highly focused on accuracy and reliability, with a clear understanding that hallucinations and misinformation erode user trust and equipped with concrete strategies to prevent them; proficient in building and maintaining end\-to\-end production ML pipelines, from data preparation through deployment and monitoring; and a strong collaborator who works effectively across engineering, product, and customer\-facing teams in a fully remote environment.
Essential Functions:
*Generative AI System Design \& Development*
- Design, build, and continuously improve LLM\-powered systems across Amira's product and operations — from internal tools to customer\-facing features
- Own RAG pipelines end\-to\-end: document ingestion, chunking strategy, embedding selection, retrieval tuning, and response synthesis
- Develop and enforce guardrails, grounding strategies, and confidence thresholds to mitigate hallucination and ensure output reliability
- Architect prompt chains and agent workflows that are robust, maintainable, and cost\-effective at scale
*Fine\-Tuning, Evaluation \& Continuous Improvement*
- Design and operate evaluation frameworks to measure system accuracy, helpfulness, hallucination rate, and task completion across generative AI features
- Fine\-tune and adapt foundation models for domain\-specific tasks, including data curation, training pipeline setup, and performance benchmarking
- Implement automated and human\-in\-the\-loop review processes to catch and correct problematic outputs
- Monitor production traffic, identify failure modes, and iterate rapidly on retrieval, prompting, and generation strategies
*Integration \& Infrastructure*
- Integrate LLM\-powered features with internal systems and third\-party platforms (e.g., Salesforce, CRM tools) via APIs, connectors, and data sync workflows
- Contribute to shared ML infrastructure and tooling used across Amira's AI systems
- Help explore and implement solutions that make generative AI economically viable within the budget constraints typical of public schools and education SaaS
*Cross\-Functional Collaboration*
- Partner with learning design, content, product, and customer success teams to ensure AI systems are grounded in accurate, up\-to\-date domain knowledge
- Translate business needs into well\-scoped generative AI solutions and communicate tradeoffs clearly to non\-technical stakeholders
Qualifications (Education and Experience):
- 2\+ years of hands\-on experience building and deploying LLM\-based systems in production
- Deep familiarity with RAG architectures: embedding models, vector databases, retrieval strategies, and response grounding
- Demonstrated experience with evaluation and benchmarking of LLM outputs — including hallucination mitigation, confidence filtering, output validation, and fallback strategies
- Practical experience with prompt engineering, prompt chaining, and/or agent orchestration frameworks (LangChain, LlamaIndex, or similar)
- Proficiency in Python and experience working with LLM APIs (open\-source, Anthropic, OpenAI, etc.)
- Experience building and maintaining ML or data pipelines in AWS or similar cloud infrastructure (Lambda, S3, RDS, etc.)
- Degree in computer science or a related technical field, or equivalent practical experience
Preferred Qualifications:
- Experience fine\-tuning foundation models or running RLHF / preference\-based feedback loops for domain\-specific improvement
- Experience in education SaaS or with education\-sector customers (districts, schools, state agencies)
- Familiarity with Salesforce or similar CRM platforms and their API/data ecosystems
- Experience with evaluation tooling, custom eval harnesses, or LLM\-as\-judge approaches
- Background working with conversational AI, chatbots, or customer\-facing generative AI features
- Proven ability to operate in a fast\-paced, goal\-oriented startup environment and manage multiple concurrent workstreams
Benefits:
- Competitive Salary
- Medical, dental, and vision benefits
- 401(k) with company matching
- Flexible time off
- Stock option ownership
- Cutting\-edge work
- The opportunity to help children around the world reach their full potential
Commitment to Diversity:
Amira Learning serves a diverse group of students and educators across the United States and internationally. We believe every student should have access to a high\-quality education and that it takes a diverse group of people with a wide range of experiences to develop and deliver a product that meets that goal. We are proud to be an equal opportunity employer.
The posted salary range reflects the minimum and maximum base salary the company reasonably expects to pay for this role. Salary ranges are determined by role, level, and location. Individual pay is based on location, job\-related skills, experience, and relevant education or training. We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, sex, sexual orientation, gender identity or expression, age, disability, medical condition, pregnancy, genetic information, marital status, military service, or any other status protected by law.
Salary Context
This $160K-$220K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Amira Learning, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($190K) sits 14% above the category median. Disclosed range: $160K to $220K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Amira Learning AI Hiring
Amira Learning has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in US. Compensation range: $220K - $235K.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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