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About This Role
Overview:
As a leading provider of outcomes-driven care for individuals and communities living with chronic conditions, Somatus is helping patients across the country enjoy More Healthy Days at Home™.
Care at Somatus goes beyond treatment. Through a whole‑person approach, we deliver outcomes‑driven integrated care and show up #SomatusStrong for our patients and teammates. We partner closely with health plans, health systems, and provider groups to support patients with, or at risk of developing, cardio, kidney, metabolic, or other chronic conditions.
We hire the brightest and boldest — talent driven by purpose and impact. Since our founding in 2016, our growth trajectory isn’t just a milestone — it’s a signal. Our leadership values culture and leads with intention as we remain dedicated to driving clinical excellence.
Does this sound like you? Keep reading.
How We’ll Support You:
We offer 25+ health, growth, and wealth work perks to help teammates be the best version of themselves, including:
- Subsidized personal healthcare coverage: Medical, Dental & Vision, plus Wellness programs
- Paid Time Off: Flexible PTO
- Professional development: CEU and tuition reimbursement
Responsibilities:
Somatus is on a mission to be the world’s best provider of integrated care for patients with or at risk of developing kidney disease. A core component of our mission is the effective and impactful use of data to support patient care. As a Machine Learning Engineer at Somatus, you will work collaboratively with our data and technology teams to help clinical, operational, and financial partners solve advanced analytical problems. Our culture is data-driven, collaborative, results-focused, and fast-moving. If you have a passion for building things and using data to impact the lives of patients, families, and communities, then we want to speak with you!* Partner with ML engineers, data engineers, software developers, product managers, data analysts, and clinical and operations team members to deliver machine learning solutions that make an impact.
- Assist with end-to-end lifecycle for AI and machine learning projects, including idea generation, data and feature engineering, model training, model evaluation, model testing, model deployment, model monitoring, and maintenance.
- Build pipelines for training, evaluating, and deploying models.
- Perform exploratory data analysis, statistical modeling, causal inference, and other advanced analytical techniques to uncover meaningful insights.
- Advise stakeholders on experimentation best practices and help design and implement A/B/n testing as needed.
- Stay current with trends and developments in the AI, machine learning, and healthcare technology communities.
Qualifications: Qualifications:* Bachelor’s or Master’s Degree with 1 or more years of experience in a machine learning-related role, or PhD
- At least 1 year of experience with Python
- Some experience with relational databases and SQL
- Understanding of software engineering best practices and software development lifecycle
- Familiarity with Git and issue tracking tools such as JIRA
- Familiarity with Linux and CLI
Preferred Qualifications:* Experience in healthcare industry and working with healthcare data
- Experience with cloud infrastructure (Azure, AWS, or GCP)
- Experience with Docker
- Experience with Natural Language Processing
- Experience with effectiveness research
Compensation:
$105,000 to $115,000 per year
We offer competitive compensation that reflects market conditions and recognizes the skills, experience, and contributions of our team members. Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, experience, and geographic location and may fall outside of the range shown above. In addition, this position may be eligible for a discretionary performance-based bonus in accordance with the Company's applicable incentive compensation plans.
This job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities required of the employee. Duties, responsibilities, and activities may change at any time with or without notice. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Other Duties : At Somatus, we celebrate what makes us unique — our people. We believe that a culture intentionally built to foster and support our unique passions, experiences, and perspectives helps fuel us in the pursuit of our mission. Somatus, Inc. provides equal employment opportunity to all individuals regardless of race, color, creed, religion, gender, age, sexual orientation, national origin, disability, veteran status, or any other characteristic protected by law. Discrimination of any type will not be tolerated.
Salary Context
This $105K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Somatus, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($110K) sits 48% below the category median. Disclosed range: $105K to $115K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Somatus AI Hiring
Somatus has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in McLean, VA, US. Compensation range: $115K - $115K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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