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About This Role
Somerville, Massachusetts · Full Time · Onsite
Base salary: 165,000–195,000 · Equity: 0.60–1.10%
Lazarys is hiring a Principal or Senior Scientist to lead LNP discovery for our systemic mRNA programs.
You will develop and optimize proprietary LNP formulations for systemic delivery, with an initial focus on extrahepatic expression, including the lung and tumor tissue. This role combines formulation science with quantitative modeling: you will design formulation campaigns, generate high-quality experimental datasets, build predictive models, and use those models to inform subsequent rounds of testing.
This is a hands-on leadership role. You will report to the CSO and supervise a Senior Research Associate focused on formulation and assay automation.
About Us
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Lazarys is developing medicines for solid tumors across RNA and cell therapy.
Our lead program is an intravenously administered mRNA therapeutic for advanced non-small cell lung cancer. We are developing delivery systems, tumor-selective expression control, and engineered therapeutic proteins intended to increase activity in tumors while limiting exposure in healthy tissues.
We are an early-stage team based in Somerville, Massachusetts, with much of our experimental work performed in-house.
Responsibilities
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- Design and execute LNP formulation campaigns across ionizable lipids, helper lipids, sterols, PEG lipids, composition, N ratio, mixing conditions, buffers, purification, concentration, and storage.
- Develop formulations for systemic mRNA delivery with an emphasis on productive extrahepatic expression, tolerability, reproducibility, and manufacturability.
- Establish and maintain characterization methods for particle size, PDI, RNA encapsulation, RNA integrity, apparent pKa, and stability.
- Develop cell-based screens across tumor and normal-cell models.
- Evaluate productive delivery, not simply cellular uptake.
- Work closely with in vivo pharmacology to compare formulation properties and in vitro performance with biodistribution, tissue expression, and tolerability.
- Determine which in vitro measurements are most predictive of in vivo performance.
- Develop predictive models for formulation data using Python.
- Apply active learning, Bayesian optimization, multi-objective optimization, or related approaches to experimental selection.
- Prospectively compare model-selected formulations with appropriate experimental baselines.
- Improve formulation reproducibility, RNA recovery, stability, material consumption, and cost.
- Supervise and develop the Senior Research Associate supporting formulation and automation.
Ideal Qualifications
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- PhD in drug delivery, chemical engineering, bioengineering, pharmaceutical sciences, chemistry, or a related field, typically with five or more years of relevant postdoctoral or industry experience for the Principal Scientist level.
- Deep hands-on experience developing LNPs or closely related nanoparticle systems.
- Strong experience with formulation development, particle characterization, mammalian cell culture, and quantitative delivery assays.
- A track record of independently developing or materially improving a nanoparticle delivery workflow.
- Strong quantitative and programming skills.
- Experience applying machine learning to chemical, formulation, or biological datasets.
- Experience with Python and relevant scientific computing tools such as pandas, scikit-learn, RDKit, PyTorch, BoTorch, or equivalents.
- Experience with sequential experimental design, active learning, Bayesian optimization, or related methods in which model outputs directly informed experiments.
- Strong judgment around assay quality, controls, replication, batch effects, uncertainty, and data leakage.
- Ability to lead a scientific program while remaining active at the bench and in code.
Particularly relevant experience
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Extrahepatic delivery, lung-targeted LNPs, lipid chemistry, primary-cell screening, automated formulation, endosomal escape assays, high-content imaging, IVIVC/IVIVE, process development, formulation scale-up, or transfer of research formulations into reproducible production processes.
Pay and Benefits
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In addition to the posted salary range, we offer generous equity options. Equity terms, including a monthly or quarterly vesting schedule, will be agreed as part of the offer.
- Compensation will depend on the skills, experience, and scope of impact you bring. If your background, experience, or compensation needs fall outside this range, we are still open to a conversation based on the scope and impact you could bring.
- $500+ monthly wellness budget for training, supplements, coaching, recovery, or other tools that help you perform at your best.
- Healthcare: We offer a broad range of medical, dental, and vision coverage options, with Lazarys covering 100% of the base policy.
- You will also have access to HSA, FSA, and 401(k) plans.
- We support visa sponsorship where appropriate, including O-1, H-1B, J-1, TN, and other employment-based pathways. We also fund team dinners, events, celebrations, and occasional offsites.
Apply Today
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Curious but not sure? Apply anyway. It takes a couple minutes, and you do not need to be actively job searching to start a conversation.
Send us your CV or resume and a short note answering two questions:
- Why Lazarys?
- What is one difficult technical problem you personally solved?
Our process typically includes an initial conversation, a technical deep dive, an onsite visit in Somerville, and references before a final offer. Selected candidates may also be invited to a short paid work trial.
We know that exceptional people do not map perfectly to a job description. If this sounds like a place where you could do important work, we would like to hear from you.
Employer Details
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Genetic Circuit Therapeutics, Inc. d/b/a Lazarys (“Lazarys”) is an equal opportunity employer. Lazarys considers qualified applicants without regard to any status protected by applicable federal, state, or local law and provides reasonable accommodations throughout the hiring process in accordance with applicable law.
Pay: $165,000.00 - $195,000.00 per year
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Vision insurance
Work Location: In person
Salary Context
This $165K-$195K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Lazarys, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($180K) sits 15% below the category median. Disclosed range: $165K to $195K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Lazarys AI Hiring
Lazarys has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Somerville, MA, US. Compensation range: $195K - $195K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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