The Role
Develop and evaluate machine learning and generative AI models using linked genomic and clinical data. Build LLM agentic systems, clinical variant interpretation pipelines, genomic representations, and reliable evaluation workflows. Investigate healthcare data quality, present findings, collaborate with bioinformatics and clinical teams, and contribute to scientific publications. The role requires strong Python, statistics, experimentation, cloud GPU, and software engineering practices, plus interest in biology and precision medicine.
Summary Generated by Built In
Who We Are & What We Do
At Helix, our mission is simple: to help everyone improve their lives through their DNA.
Ready to make a real-world impact with your skills? At Helix, we're transforming healthcare by making genomics a standard of care. We partner with health systems and life science companies to accelerate the integration of genomic data into clinical practice. Join us in building a future where healthcare is personalized, proactive, and powered by genomics.
What is special about this role?
Helix maintains one of the largest linked genomic and clinical datasets in the United States. Helix's Data Science & AI team innovates and develops state-of-the-art models that leverage this clinicogenomic dataset, for example, germline genomes linked to longitudinal clinical records, to better understand and predict a patient's journey, taking precision medicine to a new level.
You will take ownership of impactful work early on; your results will be influential, and you will collaborate closely with domain experts. You will have direct access to data that most professionals in this field only read about, along with the opportunity to develop a deep understanding of genomic and clinical data alongside senior experts who have dedicated their careers to this work.
Work you would touch in the first year:
- 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.
As an AI/ML Scientist, you will:
- 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
About you:
- 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"
Pluses:
- 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 Interview Process:
1) Recruiter Screen 2) Manager Screen 3) Tech Screens 4) Final Loop 5) Offer
Expected Pay For This Role:
There are 3 distinct parts to your Helix offer: 1) Base Salary 2) Annual Bonus 3) Equity
- 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.
Aside from working alongside brilliant, dedicated, passionate, down-to-earth, curious, warm, and thoughtful people, we also provide great benefits:
- 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
What To Expect During Your First 90 days:
- 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.
To learn how Helix collects, uses, and protects your personal information during the recruitment process, please review our Privacy Notice.
Helix is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws.
#LI-Remote
CompensationThe base pay range for this role is CA$97,000 – CA$122,500 per year.
Skills Required
- 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 a degree; strong PhD work may count
- Strong Python programming skills, including writing and debugging training loops
- Experience with Git, containers, cloud GPUs, and experiment tracking
- Working knowledge of statistics, including train/test leakage, baselines, and statistical differences
- Ability to investigate healthcare data quality and design trustworthy experiments
- Interest in biology and clinical applications
- Experience using AI tools and coding assistants with appropriate output validation
- Exposure to healthcare, genomics, or proteomics data
- Coursework or projects in computational biology, statistical genetics, or biomedical NLP
- Experience with LLM APIs, fine-tuning, or agent frameworks
- Experience with Spark, Dask, Ray, or similar large-scale data tooling, or SQL on large tables
- Familiarity with OMOP/CDM healthcare data standards
- Familiarity with ACMG/AMP variant interpretation guidelines or clinical genetics
- Public code, a paper, or a technical writeup demonstrating technical thinking
- Experience with PyTorch, Lightning, or AWS SageMaker
Helix Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Helix and has not been reviewed or approved by Helix.
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Leave & Time Off Breadth — Feedback suggests generous PTO, flexible time away, unlimited vacation, paid sick days, and paid parental leave support work–life balance. These policies are frequently highlighted alongside flexible scheduling.
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Healthcare Strength — Feedback suggests medical, dental, and vision coverage are complemented by a wellness program. Access to health-focused initiatives contributes to a comprehensive core benefits package.
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Wellbeing & Lifestyle Benefits — Feedback suggests commuter benefits, free daily meals, and onsite parking enhance day‑to‑day experience. Such perks, combined with flexible work arrangements, add lifestyle value beyond base pay.
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The Company
What We Do
Helix is a rapidly growing startup focused on personal genomics. Helix has a simple but powerful mission: to empower every person to improve their life through DNA. We’re creating an ecosystem where people can explore diverse and uniquely personalized applications provided by high-quality partners.
Why Work With Us
We are collaborators—scientists, engineers, designers, marketers, and more—working across four offices to solve complex challenges locked within the human genome. We are biased toward action as we strive to uphold integrity in sequencing, science, and communication. At Helix, transparency, collaboration, and empowerment drive us in all that we do.
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