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About This Role
Senior AI Software Engineer
About Us
Health Data Analytics Institute (HDAI) is a commercial\-stage HealthTech company. Our Intelligent Health Management System, HealthVision™, is a first\-in\-class enterprise solution leveraging real\-time predictive analytics and generative AI to deliver improved outcomes, efficiencies, and economics for health systems and value\-based care organizations.
HDAI is located in Dedham, MA near the Dedham Corporate Center Commuter Rail station on the Franklin/Foxboro line (25 minutes from Back Bay and South Station, Boston) and I\-95/128\.
Summary
We are hiring a Senior AI Software Engineer to join our Product Engineering team. In this role, you will bridge the gap between Data Science and production software by owning our full AI/ML backend. This includes a high\-throughput predictive inference engine (scoring 580\+ patient risk models in real time) and generative LLM pipelines on Amazon Bedrock (powering clinical summaries and diagnostic discovery).
You will work across the entire backend stack as priorities shift, developing deep domain expertise while ensuring complete system coverage.
We expect you to use AI tools daily to multiply your output.
Responsibilities
- Inference engine. Own the system that scores patients against hundreds of predictive models in production. Understand the scaling concerns (580\+ models, memory pressure, compute efficiency) and drive architectural improvements.
- ML productization. Work with Data Science to take model artifacts (coefficient files, scoring logic built in R) and ensure they run correctly in the production Python inference pipeline. Understand the underlying model logic and statistical outputs well enough to validate data and parity between the R development environment and production output. Build tooling that automates validation and reduces the manual reimplementation cycle.
- LLM applications. Build and maintain production LLM integrations on Amazon Bedrock (Claude) for AI summaries, PDD, and conversational interfaces. Manage prompt lifecycle: design, versioning, testing, and evaluation. Handle the specific challenges of LLM applications in production: token management, output validation, evidence citation, structured output parsing, and graceful degradation.
- Production AI engineering. Build and maintain production Python services on AWS (Lambda, DynamoDB, SQS, S3\). Work across the existing stack including FastAPI and legacy services during migration to serverless.
- Evaluation and quality. Build testing and evaluation frameworks for both model inference and LLM outputs. Define what “correct” means for AI\-generated clinical content in collaboration with Clinical and Data Science teams. Write tests for critical paths. Monitor production output quality and catch regressions.
- Cross\-functional collaboration. Partner with Data Science on model release planning, validation, and new prompt development. Work with Platform Engineering on deployment infrastructure. Collaborate across the full Product Engineering team on features that span frontend and backend.
Qualifications
Required:
- 7\+ years of experience as a software engineer, ML engineer, or AI engineer building production systems.
- Strong Python proficiency for production service development.
- Experience building and operating services on AWS (Lambda, API Gateway, DynamoDB, SQS, S3\) or equivalent cloud platforms.
- Experience in at least one of these areas, with willingness to grow into the other:
*ML engineering:* taking model artifacts from data science and deploying them to production. You understand model scoring, feature engineering, and what it takes to validate that a production implementation matches the original model.
*LLM engineering:* building production LLM applications (not just prototypes). You have worked with model APIs (Bedrock, OpenAI, or similar), designed prompt architectures, and handled the failure modes of generative AI in production.
- AI\-native engineering practice. You use AI tools (Claude Code, Cursor, or similar) as a core part of your daily workflow.
- Comfort with ambiguity. Requirements evolve as we scale to new health system partners; you make sound decisions with incomplete information.
- Clear written and verbal communication.
Preferred:
- Experience across both ML engineering and LLM engineering (see above).
- Experience with Amazon Bedrock or similar managed LLM services.
- Experience with R or familiarity reading R code (our data science team works in R).
- Experience with model serving at scale (hundreds of models, multi\-tenant environments).
- Healthcare or regulated industry experience.
- Experience building evaluation/testing frameworks for AI system outputs.
- Experience with FastAPI, event\-driven architectures (SQS, SNS), or serverless patterns.
- Familiarity with Terraform and infrastructure\-as\-code.
Equal Opportunity
HDAI is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
Pay: $160,367\.00 \- $185,457\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Health savings account
- Paid time off
- Retirement plan
- Tuition reimbursement
- Vision insurance
Work Location: Hybrid remote in Dedham, MA 02026
Salary Context
This $160K-$185K range is below 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 Health Data Analytics Institute, 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 $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 ($172K) sits 20% below the category median. Disclosed range: $160K to $185K.
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.
Health Data Analytics Institute AI Hiring
Health Data Analytics Institute has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dedham, MA, US. Compensation range: $185K - $185K.
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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