Member of Technical Staff (ML)
solstice · New York City · onsite
USD 230,000 to 350,000 per year
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 quality of the models, representations, and datasets behind Solstice's products. You will take a problem from error analysis and a credible baseline through a controlled experiment, implementation, and a monitored production release.
Your core work is understanding why a system makes the wrong prediction, retrieves the wrong evidence, or fails on an important class of inputs, then making a defensible improvement. The right intervention may be better data or labels, a different retrieval or ranking approach, model selection, or targeted training and fine-tuning. You will choose the approach based on evidence and the product's constraints.
You will work closely with Applied AI engineers, who own complete model-enabled features and workflows, and Fullstack AI engineers, who build the shared product and agent runtime. You will own the model and data improvements those systems use, including how they are validated and monitored after release.
WHAT YOU'LL BE WORKING ON
- Turn product problems into clear ML objectives, with useful baselines, relevant error costs, and explicit constraints on latency, cost, and reliability.
- Investigate data and model failures through error analysis and targeted slices. Improve label quality, dataset coverage, and handling of noisy or delayed reviewer feedback.
- Develop and compare retrieval, ranking, classification, and other model-based approaches where they solve a real product problem.
- Design controlled experiments with reproducible configurations, appropriate data splits, leakage checks, and clear interpretation of uncertainty and generalization.
- Implement or adapt learning components, representations, and training or fine-tuning methods when the evidence supports them. Understand the learning behavior and tradeoffs behind the code.
- Connect offline improvements to customer outcomes. Work with Applied AI and product partners to test whether a measured gain improves the actual task.
- Ship and operate improvements with versioned data and models, staged releases, quality monitoring, rollback criteria, and attention to changing input distributions.
- Explain what an experiment establishes, where it fails, and what to try next. Keep unsuccessful experiments useful and reproducible.
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 experience using modern ML tools to implement, inspect, and improve model behavior.
- 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, NLP, model adaptation, or fine-tuning, with the ability to learn adjacent approaches.
- 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 $230,000 to $350,000, 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.