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About This Role
Imagine if you had the skills, knowledge, and teammates to both understand the root of the world’s most pressing problems and build the technologies and companies best positioned to solve them. RA Capital has done exactly that for more than two decades, backing bold ideas in medicines to further human health and now expanding into Planetary Health to improve how efficiently we utilize the world’s precious resources.
RA Capital is among the leading providers of capital and services to the most promising innovators in the world. We invest flexibly—seed to IPO and beyond, anywhere in the world—with $10 B+ under management and a culture that prizes curiosity, rigor, and collaborative debate. We are investors who not only fund companies but get elbow deep in building them. From helping them recruit talent to helping them recruit patients for their studies to helping match them to strategic partners and even going to Washington to win reforms, RA Capital’s large team has people with nearly every relevant expertise one might need to turn an idea into a cure that actually helps people.
If you live for first-principles problem-solving with great colleagues, thrive on complexity, and want to do meaningful work that ripples across industries and ecosystems, you’ll feel at home at RA Capital. Here, questions are welcomed, ideas are tested, and victories are shared. Even our lawyers are creative and engaging. And don’t get us started on our compliance team’s wicked sense of humor; nothing about what we do is boring.
Are you ready to bring your creativity, discipline and collaborative spirit to help us invent the future? Join us and you’ll collaborate daily with investors, founders, physicians, biologists, engineers, economists, and reform advocates who think in systems and act with urgency.
Join us to invent a happier, healthier, more productive future - and have fun doing it.
About the Team
RA Capital’s Healthcare Data Strategy team uses data-driven analysis to inform investment decisions and improve core business processes. The team’s work spans portfolio management, risk management, and data science, leveraging complex healthcare and financial datasets to identify important trends, patterns, and decision drivers.
The team operates in a fast-paced, collaborative environment and works across RA Capital, including with the Investment Team, RA Ventures, Legal, Finance, Compliance, and TechAtlas. Team members also have opportunities to work directly with senior leadership and portfolio managers.
About the Role
As a Senior Applied AI Scientist, you will design and implement AI-driven systems that transform complex, unstructured information into reliable, decision-relevant insights. Working at the intersection of applied AI, data science, and investment research, you will partner closely with investment professionals to translate ambiguous questions into technical solutions and actionable conclusions.
This is a hands-on individual contributor role with responsibility for independently owning projects from initial problem definition through deployment and ongoing improvement.
Responsibilities
- Design and build systems that transform unstructured healthcare, scientific, and financial information into structured, usable insights.
- Develop and deploy AI-driven workflows using approaches such as NLP, retrieval-augmented generation, knowledge graphs, embeddings, entity extraction, and agentic systems.
- Own applied AI projects from problem definition and prototyping through deployment, evaluation, and iteration.
- Develop benchmarks and evaluation frameworks to assess accuracy, relevance, reliability, and potential failure modes.
- Partner with investment professionals and other stakeholders to identify high-value use cases and incorporate findings into investment research and decision-making.
- Translate complex healthcare, financial, and analytical information into clear narratives, tools, and visualizations for technical and nontechnical audiences.
- Help establish best practices for data quality, testing, documentation, reproducibility, and responsible AI use.
Qualifications
- Bachelor’s or advanced degree in computer science, data science, engineering, statistics, applied mathematics, computational biology, or another related quantitative or technical field.
- At least 4 years of professional experience in applied machine learning, data science, software engineering, data engineering, or a related discipline, including at least 2 years working with NLP, information-retrieval, or LLM-based systems.
- Hands-on experience with one or more of the following: NLP workflows, knowledge graphs, vector databases, embedding-based retrieval, entity extraction and linking, RAG, or agentic AI systems.
- Experience taking AI or machine-learning solutions beyond initial prototyping, including testing, deployment, evaluation, or ongoing improvement.
- Strong Python and SQL skills, along with familiarity with software-engineering practices such as version control, testing, and documentation.
- Strong understanding of AI and machine-learning fundamentals, evaluation methods, and the limitations of LLM-based systems.
- Ability to independently structure ambiguous problems and communicate complex findings clearly to senior stakeholders.
- Must be authorized to work in the United States without current or future sponsorship or transfer of sponsorship.
- Must be based in Massachusetts or willing to relocate and able to work a hybrid schedule from RA Capital’s Boston office.
Preferred Qualifications
- Experience working with healthcare, life sciences, investment management, financial services, or other complex domain-specific data.
- Familiarity with scientific literature, clinical-trial information, company filings, market research, or other sources relevant to healthcare investing.
- Experience with Spark, PySpark, cloud-based AI platforms, or production data pipelines.
- Experience working directly with investment, research, business, or executive stakeholders.
Compensation
RA Capital Management is committed to fair and equitable compensation practices. Pay for this position will be based on experience, skills, qualifications, and current market conditions.
In addition to base salary (listed below), employees may be eligible for performance-based bonuses and a comprehensive benefits package that includes health insurance, retirement contributions, and paid time off.
Total Rewards
We take pride in offering a comprehensive employee benefits program as a key part of overall compensation. Our Total Rewards include:
- Employer-paid monthly premiums for health, dental, and vision coverage
- Wellness benefits and programs to support physical and mental well-being
- Resources and perks that enhance work-life balance and financial security
We’re dedicated to helping our employees thrive professionally and personally.
Massachusetts base pay range
$140,000 - $180,000 USD
RA Capital is an equal opportunity employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. If you require an accommodation during the interview process, please reach out to [email protected] for assistance.
Salary Context
This $140K-$180K range is below the median 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 RA Capital Management, 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 ($160K) sits 25% below the category median. Disclosed range: $140K to $180K.
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.
RA Capital Management AI Hiring
RA Capital Management has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, US, Boston, MA. Compensation range: $180K - $180K.
Location Context
AI roles in Boston pay a median of $215,000 across 323 tracked positions.
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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