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About This Role
Staff Engineer AI/ML
*Remote, Anywhere in the US*
About AnswersNow
At AnswersNow, we are trailblazing the future of autism therapy, making it more immediate, accessible, and effective for families everywhere. Our innovative virtual ABA therapy platform is thoughtfully designed by clinicians to recreate the focused, supportive environment of in\-person therapy, complete with distraction\-free features and interactive activities that enhance engagement and progress.
Our team operates fully remote—meaning you’ll have the flexibility to work from the comfort of home. If you're ready to make a meaningful impact and join a team that's reshaping autism therapy, we’d love to hear from you!
Why this role matters:
We're looking for a Senior Engineering Manager who knows that great engineering leadership is about growing people, removing obstacles, and making the team better — not writing the most code. You've led teams before. You know how to hold a high bar for craft while keeping engineers energized and growing. You understand that a team of four highly effective engineers with strong habits will outperform a team of twelve with unclear expectations.
You'll manage a team of Engineering Team Leads, each of whom runs a squad of full\-stack engineers. You own the health of the engineering organization: hiring, performance, culture, delivery, and the processes that keep everything running. You partner closely with the CTO, product leadership, and clinical stakeholders to ensure engineering is building the right things, the right way.
This is a player\-coach role in spirit — you should be technically credible and able to engage meaningfully in architecture discussions — but your primary output is team output, not individual code.
Job Details
- W2 Employee
- Full\-Time
- 100% Remote
Job Requirements
- 8\+ years of professional software engineering experience, with at least 3 years in a senior or staff\-level role
- Deep expertise in JavaScript and TypeScript in a production full\-stack environment
- Strong command of React, Node.js/Express, and Postgres — you can reason about performance, reliability, and maintainability at each layer
- Proven experience designing and owning distributed systems or data pipeline architectures on AWS (ECS, RDS, S3, Lambda experience valued)
- Track record of technical mentorship — you've made engineers around you measurably better
- Excellent written and verbal communication; you can write an architecture doc, run a design review, and brief a non\-technical executive in the same day
- Experience working in or leading engineering at a growth\-stage startup — you understand the balance between velocity and sustainability
- Familiarity with ETL pipelines, AI/LLM integration patterns, or data\-intensive systems is a strong plus
- Significant experience architecting and deploying production\-grade ML/AI systems, including LLM integration and model lifecycle management.
Nice to have:
- Experience with healthcare or regulated data environments (HIPAA compliance, PHI handling)
- Familiarity with event\-driven architectures, message queues, or streaming pipelines
- Prior experience as an engineering manager who returned to an IC track (player\-coach background welcome)
- Contributions to open source or engineering blog posts / conference talks
What You’ll Do
Technical Leadership
- Own and evolve the architecture of our core platform: React frontend, Node.js/Express APIs, Postgres, and AWS ECS infrastructure
- Drive our JavaScript TypeScript migration, establishing patterns, tooling, and standards the team can follow
- Design and extend our ETL pipeline and config\-driven AI integration framework, enabling engineers to ship AI\-powered features faster and more reliably
- Lead technical design reviews, create architecture decision records (ADRs), and ensure engineering decisions are documented and defensible
- Identify and resolve systemic technical debt — not just flagging it, but owning the plan to address it
- Define and execute the technical strategy for integrating ML/AI models into our core platform, ensuring scalability, performance, and reliability.
Engineering Culture \& Mentorship
- Mentor engineers across all levels — pair, review, coach, and grow the team's collective capability
- Establish and maintain engineering standards: code quality, testing practices, observability, on\-call runbooks, and incident response
- Serve as a technical resource and thought partner for product, clinical, and business stakeholders
- Help define the engineering hiring bar and participate in the interview process
Delivery \& Operations
- Partner with engineering leadership and product to scope, sequence, and de\-risk large technical initiatives
- Lead by example on production support — define runbooks, own post\-mortems, and drive systemic fixes from incidents
- Work across UI, API, DB, and infrastructure layers with genuine depth in at least two
What we Offer
- $151,000\- $178,500 annual salary
- Fully remote – work from anywhere in the U.S.
- Flexible hours with an async\-friendly team culture
More About AnswersNow
AnswersNow welcomes applicants of all backgrounds, experiences, and abilities. We believe a diverse team is a strong team, and are committed to provide a fair and equitable experience for every candidate. If you require reasonable accommodations at any stage, we encourage you to reach out. We’re here to support!
Learn more about us at getanswersnow.com.
Compensation Range: $150K \- $215K
Salary Context
This $150K-$215K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At AnswersNow, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $150K to $215K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
AnswersNow AI Hiring
AnswersNow has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $215K - $215K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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