• Training and evaluating generative models on longitudinal clinical records fused with variant-level germline genomics, to forecast disease onset and patient trajectory
• Building and measuring LLM agentic solutions that pull structured findings out of clinical text and the published literature
• Contributing to our multi-agent pipeline for clinical variant interpretation, which is in production today and used by our clinical genomics team on every uncurated variant
• Feature and representation work on genomic data: turning variant and gene-level information into something a model can actually use
• Running experiments carefully enough to ensure we can trust the results, which primarily means designing the comparison correctly before launching the job.

• Own well-scoped pieces of a larger modeling effort: implement, train, evaluate, and report
• Write experiment code that colleagues on the team can read, rerun, and trust
• Build and maintain evaluation pipelines, and be honest about what they do and do not measure
• Investigate data quality issues. In healthcare data, these are not a distraction from modeling work; they are a necessity
• Present your results to the team and leadership, including the ones that did not work, and have opportunities to represent the work externally over time
• Collaborate as the AI/ML voice across bioinformatics, clinical, and engineering stakeholders
• Draft scientific writeups and internal summaries of the team's findings, and carry results through to papers or conference presentations

• MS or PhD in machine learning, computer science, statistics, computational biology, bioinformatics, or a related quantitative field, or equivalent experience
• 2 to 5 years of applied machine learning experience beyond your degree. Strong PhD work counts
• Solid Python skills. You can write a training loop, read someone else's, and debug it when the loss goes flat
• You get real leverage out of AI tools and coding assistants, and you know when to trust their output and when to check it
• Comfortable with the practical parts: git, containers, running jobs on cloud GPUs, tracking experiments
• Working knowledge of statistics: you know what a train/test leak is, why a baseline matters, and when a difference between two numbers is not a difference
• Genuine interest in biology and the clinical problem. You do not need to arrive knowing genomics, but you do need to want to learn it
• You are comfortable saying "I do not know yet"

• Any exposure to healthcare, genomics, or proteomics data: EHRs, claims, sequencing, imaging, registries
• Coursework or projects in computational biology, statistical genetics, or biomedical NLP
• Experience with LLM APIs, fine-tuning, or agent frameworks, especially where you had to measure whether the thing worked
• Experience with large-scale data tooling (Spark, Dask, Ray, or similar) or with SQL on genuinely large tables
• Familiarity with healthcare data standards (OMOP/CDM)
• Familiarity with variant interpretation and classification guidelines (ACMG/AMP) or clinical genetics more broadly
• Public code, a paper, or a technical writeup: something we can read that shows how you think
• PyTorch, Lightning, AWS SageMaker

• Expected Helix Base: $97,000 - $122,500
• Expected Helix Discretionary Annual Bonus: 10% of your annual salary
• Equity: We offer generous equity at Helix. If you receive a Helix offer your recruiter will book dedicated time with you to educate you on our equity model.

• Comprehensive Health Insurance with Date of Hire eligibility
• 12 weeks Helix Paid Parental Leave option
• Comprehensive Well-Being Benefits
• Flexible PTO
• Remote options for many roles and a home office stipend

• First 30 days: you’ll spend time learning the Helix way, completing training and onboarding for your roles, and getting introduced to your team and relevant stakeholders. You’ll also gain a deeper understanding of our customers, our products, the impact we make in the lives of our communities, and how to thrive at Helix through participation in Helix U.
• Day 30 - 60: you’ll spend time contributing to projects, deeply familiarizing yourself with team and company processes, and developing a deeper understanding of Helix’s products, services and capabilities.
• Day 60 - 90: you’ll build your OKRs with your manager, start to take ownership of projects and initiatives on your team, and begin to demonstrate your impact on the Helix mission.

Compensation