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About This Role
Ellipsis Health is creating cutting\-edge AI/ML products that solve healthcare staffing challenges and administrative burdens using conversational AI and our patented voice biomarker technology—helping deliver better healthcare for everyone. We are headquartered in Silicon Valley and are funded and supported by some of the most preeminent venture capital teams.
In this role, you will be a core contributor and technical leader within our AI Research \& Engineering team. Operating at the sweet spot between cutting\-edge scientific research and production\-grade systems, you will drive the conceptualization, architecture, and deployment of frontier agentic systems. You will collaborate directly with core infrastructure and platform teams to translate algorithmic breakthroughs in LLMs and agentic AI into robust, secure, and low\-latency clinical applications.
Responsibilities:
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- Technical Leadership \& Vision: Act as the technical authority for the Agentic AI roadmap, steering engineering pods from initial research prototyping through to production scale.
- Advanced Agentic Architecture: Design, build, and optimize scalable systems and orchestration pipelines capable of deep multi\-step reasoning, dynamic memory allocation, long\-context evaluation, and high\-fidelity tool utilization.
- Research\-to\-Production Translation: Bridge the gap between frontier machine learning research and enterprise software design. Implement and adapt state\-of\-the\-art techniques in post\-training fine\-tuning, preference alignment, and automated prompt optimization.
- Rigorous Evaluation \& Benchmarking: Architect and maintain multi\-dimensional quantitative evaluation frameworks and continuous testing infrastructure. Implement state\-of\-the\-art LLM\-as\-a\-judge rubrics, and statistical tracking to evaluate agent capabilities and guarantee zero\-regression product updates.
- Data Mix Optimization: Develop and scale automated data curation pipelines to extract, filter, and structure user feedback signals, execution logs, and expert labels into ultra\-high\-quality training datasets for model fine\-tuning.
- Cross\-Functional \& Clinical Collaboration: Work closely within a highly matrixed, collaborative organization alongside clinical experts, product managers, and software engineers to ensure AI platforms operate safely and ground outputs in validated healthcare protocols.
- Mentorship \& Engineering Excellence: Foster a culture of technical rigor by setting high coding standards, leading design reviews, standardizing tooling, and mentoring other ML and software engineers across the team.
Minimum Qualifications
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- Bachelor’s degree in Computer Science, Machine Learning, Statistics, Engineering, or equivalent practical experience.
- 5\+ years of practical software development experience with a heavy emphasis on product design, core system architecture, and shipping scalable software platforms (or 2\+ years of post\-PhD industry experience in a dedicated AI research/engineering role).
- Advanced programming proficiency in Python and deep hands\-on expertise with distributed machine learning or deep learning frameworks (e.g., PyTorch, JAX, or TensorFlow).
- Proven track record of architecting, implementing, and deploying complex LLM\-powered applications, multi\-agent orchestrations, or autonomous systems in a production environment.
Preferred Qualifications
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- Master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative technical field.
- 8\+ years of industry experience specializing in advanced data structures, algorithms, distributed systems, and heavy\-duty scalable ML infrastructure.
- 3\+ years of technical leadership experience, including leading complex technical project workstreams, defining organizational engineering directions, or directly mentoring technical talent.
- Experience working within complex, highly collaborative matrixed organizations on high\-acuity, deeply regulated technical integrations (e.g., healthcare, fintech, or aerospace).
- Research Excellence: A solid track record of contributions or publications at top\-tier machine learning venues (e.g., NeurIPS, ICML, KDD) with a specific focus on agentic frameworks, reinforcement learning, or data benchmark.
Salary and Benefits:
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We offer competitive salary and benefits, including 401(k) matching, health, vision, and dental insurance, and very flexible paid time off.
The typical salary range for this role is $160,000 to $210,000 USD, depending on skills, qualifications, and relevant experience.
Background Checks:
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As a health technology company, we reserve the right to run background checks on candidates to whom we extend offers, in compliance with applicable laws. We evaluate candidates holistically and comply with all “ban the box” regulations.
Assistance:
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If you have a disability or require accommodations during the application or recruitment process, please contact [email protected].
Compensation Range: $160K \- $210K
Salary Context
This $160K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Ellipsis Health, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $160K to $210K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Ellipsis Health AI Hiring
Ellipsis Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $210K - $210K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,258 tracked positions. That's 26% 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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