Interested in this AI/ML Engineer role at Solstice?
Apply Now →Skills & Technologies
About This Role
Why you should join us
Solstice builds AI software that helps life sciences teams create, refine, and review pharmaceutical marketing content. We have worked with leading life sciences manufacturers and more than 50 pharma brands. Our backers include Transformation Capital, Twelve Below, Virtue, and the founders of Datavant, Commure, and Paradigm.
The technical problems are concrete: find the right evidence in complex scientific material, identify claims accurately, and improve generated content using reviewer feedback and real product outcomes. You will work with proprietary data and a team that can put your improvements in front of customers.
About the role
As a Member of Technical Staff in Machine Learning, you will improve the models, representations, and data that power Solstice's AI agents for creating, refining, and reviewing pharmaceutical marketing content. Your work will help agents find supporting evidence, use tools accurately, and incorporate reviewer feedback into better outputs.
You will investigate failures across complete agent runs, form a testable hypothesis, implement a change, and establish whether it improves the customer's task. The intervention might be better training examples, a retrieval or ranking improvement, a different model or context strategy, or targeted fine-tuning. You will choose an approach based on evidence and test its quality, reliability, latency, and cost before taking it into production.
You will work directly with Applied AI engineers, who own the complete feature and workflow, and Fullstack AI engineers, who build the shared runtime, APIs, and product experiences. You will build prototypes and production components together, with primary responsibility for model and data improvements and the evidence that they generalize.
What you'll be working on
- Improve model behavior inside agents. Diagnose failures in evidence selection, tool choice and arguments, and revisions based on feedback. Work with Applied AI to separate model or data limitations from missing context, tool failures, or workflow design problems.
- Make retrieval and grounding more accurate. Develop and compare retrieval, reranking, and claim-to-evidence matching approaches across scientific documents and approved content. Measure whether the evidence actually supports the claim and helps the agent complete the task.
- Turn agent runs and reviewer feedback into useful data. Build curated datasets from successful and failed runs, corrections, and targeted examples. Track provenance, resolve ambiguous labels, and keep held-out evaluation cases separate from training and tuning.
- Implement and test substantive improvements. Compare model choices, representations, context strategies, and learning methods with credible baselines and controlled experiments. Use targeted fine-tuning or other model adaptation when the available data and results justify it.
- Measure complete-task outcomes. Evaluate intermediate tool behavior and the resulting artifact. Check repeated-run reliability and generalization to unfamiliar documents and task variants. Calibrate automated judgments against human review and track quality alongside latency and cost.
- Ship improvements with the product team. Integrate changes into production Python services and agent workflows. Use versioned data and models, staged releases, quality monitoring and rollback criteria, then investigate whether offline gains hold up in use.
What we're looking for
- Strong ML fundamentals and hands-on implementation skills. You can reason about learning objectives, optimization, regularization, data quality, and generalization.
- Strong Python and hands-on experience building or improving LLM systems, retrieval systems, or agents. You can inspect tool-call traces, reproduce failures, and implement the experiment or production change needed to investigate them.
- Evidence of taking a data or model improvement into production and understanding what happened after deployment.
- Sound experimental judgment: explicit hypotheses, credible comparisons, meaningful baselines, and care with leakage, noisy labels, and misleading aggregate metrics.
- Depth in at least one relevant area, such as retrieval and ranking, representation learning, model adaptation, or learning from human feedback, with the ability to apply that depth to multi-step agent tasks.
- Judgment about the source of a failure. You can explain when to change the data or model, when to improve context or a tool interface, and how to design a comparison that distinguishes those explanations.
- Practical software engineering skills for reproducible experiments, tested components, production integration, and debugging.
- Clear communication and personal ownership. You can explain the tradeoffs, work with product and engineering partners, and follow through when results challenge your assumptions.
Experience with scientific documents, human review feedback, or regulated enterprise products is useful. Healthcare or pharma experience is welcome but not required. We value demonstrated implementation and experimental judgment; a particular degree, publication record, or framework is not a substitute for that evidence.
Benefits
- Health, dental, and vision insurance
- Ground-floor equity opportunity
- Visa sponsorship for O-1, H-1B, and TN visas
- Unlimited PTO
- 401(k) with a 4% company match
- In-office lunches five days a week
- Professional growth stipend
- Relocation support
Location and compensation
New York City. Full time. On-site.
The base salary range is $200,000 to $300,000, plus equity, depending on experience and scope of responsibility. This is the same NYC compensation band as our Member of Technical Staff (Applied AI) role.
Compensation Range: $200K - $300K
Salary Context
This $200K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% of the market. At Solstice, 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 $212,160 based on 13,809 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($250K) sits 18% above the category median. Disclosed range: $200K to $300K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Solstice AI Hiring
Solstice has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $300K - $300K.
Location Context
AI roles in New York pay a median of $221,000 across 3,262 tracked positions. That's 4% above the national 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.