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Senior ML Engineer, Clinical Automation
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Hybrid in Palo Alto (Mon / Wed / Thurs) · $170K–$230K base \+ meaningful early\-stage equity \+ full benefits
About Sodalis
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Specialty pharmacy is where the most complex and expensive drugs reach the most in\-need patients, and it still runs on faxes, phone tag, and manual data entry. Patients wait weeks for therapies that can't wait.
Sodalis is building AI agents that run those operations end to end: reading and validating prescriptions, handling phone calls, and moving prescriptions through eligibility and fulfillment. The ambition goes beyond that. We want to be the rails every specialty prescription runs on, and the first system to perform clinical review at the standard of a pharmacist.
We're already in production, processing tens of thousands of prescriptions a week. Backed by Gradient Ventures (Google's AI fund), and founded by veterans of Apple's Applied ML, Included Health, and Avella Specialty Pharmacy (scaled to $1\.5B\+ before its acquisition by OptumRx).
The Opportunity
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Before a specialty prescription is dispensed, a pharmacist has to review it: the right drug and dose for this patient, interactions, contraindications, whether the therapy makes sense given everything else going on. That's the problem you'd own: AI that performs that same clinical review and presents it for verification. Not to replace clinical judgment, but to do the legwork so pharmacists can operate at the top of their license.
It's unsolved and genuinely hard. The system has to know what it knows and defer cleanly when it doesn't, earn a pharmacist's trust with reasoning they can verify rather than a black\-box score, and be measured against clinical ground truth that no existing benchmark captures. Clinical experts label the data that establishes it, and you build the evaluation on top.
You'd own this and the intelligence layer beneath it: the models, pipelines, and eval infrastructure that turn messy clinical inputs into structured, trustworthy actions, plus the data flywheel that improves them with every prescription. It's the moat, and you'd be the ML engineer who owns it end to end, working directly with the founders.
What you'll do
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- Own extraction and structuring of messy clinical inputs (faxed prescriptions, prescriber directions, benefit data), turning free\-form documents into accurate, structured data the pipeline can act on.
- Build the clinical review that performs the checks a pharmacist would (interactions, contraindications, dose and therapy appropriateness), as explainable reasoning they can verify and trust.
- Build the confidence scoring and routing that decides what's safe to automate and what needs a human in the loop.
- Stand up the eval infrastructure, curated datasets, and production feedback loops that drive continuous accuracy gains: the data flywheel.
- Choose the right tool for each problem, from prompting and fine\-tuning LLMs to classical ML, biased toward what ships and holds up in production.
- Partner closely with pharmacy operations and engineering to understand the real work, and turn what you learn into better models.
What we're looking for
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- 5\+ years building ML systems that run in production: you've owned models end to end, not just trained them in a notebook.
- Deep experience across information extraction, NLP, document understanding, and LLMs.
- You own the machinery around the model: eval harnesses, data pipelines, and the infrastructure it runs on, and you improve it against real metrics.
- Comfortable with messy, high\-stakes, real\-world data where correctness matters.
- Pragmatic about models: you reach for the simplest approach that works, not the fanciest.
- You've shipped 0 1 in startup environments and are comfortable owning ambiguity.
- Curiosity about the clinical domain and eagerness to learn it deeply, working closely with pharmacists. No healthcare background required.
How we work
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We're a handful of people going all in on a problem we think is worth it, holding a high bar and shipping work that reaches real patients. It's intense, and it isn't for everyone. We want people who want to build something that matters, alongside others who care as much as they do. If that's you, we should talk.
Compensation Range: $170K \- $230K
Salary Context
This $170K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At SODALIS, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($200K) sits 7% below the category median. Disclosed range: $170K to $230K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
SODALIS AI Hiring
SODALIS has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Palo Alto, CA, US. Compensation range: $230K - $230K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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