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About This Role
OUR PURPOSE
Our mission is to build a healthier and more connected world with precision health and genealogy services.
We empower individuals with actionable insights into their genetic makeup, fostering a deeper understanding of their ancestry, health, and wellness. By integrating the experience of Gene by Gene Laboratory Services, FamilyTreeDNA genealogy, and myDNA reporting services, we strive to deliver cutting\-edge genetic testing and personalized solutions that inspire informed decisions and enhance quality of life. Our team is dedicated to advancing the field of genomics through innovation, research, and a commitment to excellence.
OUR VALUES
All employees are expected to demonstrate our values of Innovate, One Team, and Integrity when carrying out the accountabilities and responsibilities of their role.
This how we show up every day for ourselves, our colleagues and our customers and strategic partners to deliver our vision and strategic goals.
POSITION OVERVIEW
We are seeking a Senior AI Developer to join our engineering team. In this senior role you will help shape and execute our AI/ML strategy, guiding our journey from early generative\-AI capabilities into a mature, production\-grade AI practice. You will integrate generative\-AI features into our products and internal platforms, combine retrieval\-augmented generation (RAG) with knowledge graphs to ground model outputs in our domain data, and own AI features end\-to\-end — model selection, prompt engineering, retrieval, fine\-tuning where appropriate, deployment, observability, cost governance, and compliance posture. As a senior individual contributor, you will also provide architectural direction and code\-level guidance to existing engineering teams who own day\-to\-day delivery of supporting backend and data\-layer work.
COMPLIANCE \& GOVERANCE
This role operates in a regulated environment. The Senior AI Developer is expected to understand how regulatory obligations apply to AI/ML systems specifically — training data, PII handling, model output controls, audit logging, evidence retention, and the limits regulation places on third\-party model usage — and to produce the operational evidence that carries our AI capabilities through audits.
ACCOUNTABILITIES AND RESPONSIBILITIES
- Helps set technical direction for AI/ML — evaluates models, frameworks, vector stores, graph databases, evaluation tooling, and orchestration patterns; makes recommendations and leads adoption.
- Designs and implements production generative\-AI features using managed foundation\-model services, applying guardrails, contextual grounding, structured output, tool use, and agentic workflow patterns.
- Builds retrieval\-augmented generation (RAG) pipelines — document ingestion, chunking, embeddings, vector search, hybrid retrieval, and reranking — selecting the storage approach that best fits each use case.
- Designs and operates knowledge graphs to model the domain — schema and ontology design, entity resolution, relationship extraction, and integration with LLM workflows (GraphRAG, hybrid graph \+ vector retrieval).
- Trains and fine\-tunes models where it produces measurable lift, including dataset preparation, supervised and parameter\-efficient fine\-tuning, baseline evaluation, and deployment.
- Provides architectural direction and code\-level guidance to existing .NET and SQL engineering teams responsible for backend services and data\-layer integration with AI features.
- Defines and enforces LLMOps / MLOps practices: prompt and model versioning, evaluation harnesses, regression testing, latency and cost SLOs, and reproducible training pipelines.
- Implements observability for AI systems and makes the data actionable across token usage, latency, hallucination and refusal rates, contextual\-grounding faithfulness, cost\-per\-request, and quality metrics.
- Builds and operates AI systems for audit\-readiness — data lineage, prompt and model version traceability, decision logging, access controls, and evidence collection.
- Mentors fellow engineers, leads code review, contributes to architecture decision records, and helps shape the team's AI engineering standards.
- Partners with security and compliance to ensure AI systems meet data privacy, PII handling, prompt injection defense, and responsible\-AI requirements throughout the model lifecycle.
POSITION REQUIREMENTS
- Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent professional experience.
- 10\+ years of professional software engineering experience.
- 2\+ years building production AI/LLM features on a managed foundation\-model platform.
- Demonstrable experience training and/or fine\-tuning models — supervised fine\-tuning, parameter\-efficient fine\-tuning (LoRA, QLoRA), or classical ML — including dataset preparation, evaluation, and deployment.
- Production experience with knowledge graphs — schema and ontology design, a graph database, and at least one graph query language (Cypher, SPARQL, or Gremlin).
- Demonstrable production experience in regulated environments. Compliance is a hard requirement for this role.
- 3\+ years of production cloud experience including at least one managed AI service.
- Solid grounding in prompt engineering, RAG, embeddings, vector search, guardrails, contextual grounding, and LLM evaluation methodology.
- Ability to provide architectural direction and technical guidance to existing engineering teams; senior IC influence rather than line management.
- Strong testing discipline — unit, integration, and contract testing, plus AI\-specific evaluation harnesses.
- Excellent written and verbal communication; ability to explain AI tradeoffs to non\-technical, legal, and compliance stakeholders.
OTHER COMPENTENCIES AND TECHNOLOGIES
- Hands\-on experience with managed AI services across major cloud providers — for example Amazon Bedrock, Amazon SageMaker, Google Vertex AI, or Azure AI Foundry — is a plus. Familiarity across more than one provider is preferred.
- Production C\# / .NET experience with ASP.NET Core and Entity Framework Core.
- Production SQL experience on Microsoft SQL Server and PostgreSQL — schema design, query tuning, indexing, and performance troubleshooting.
- Comfort working with on\-premises database infrastructure and hybrid (on\-prem / cloud) data architectures.
- Python proficiency for ML workflows.
- Production Infrastructure\-as\-Code experience (Terraform, CDK, CloudFormation, or Pulumi).
- Experience designing and consuming REST APIs, including modern authentication patterns (OAuth 2\.0, OIDC, JWT).
- Broader machine learning experience: classical/predictive ML, deep learning frameworks, or experience with managed training platforms.
- GraphRAG patterns, entity resolution, and automated knowledge\-graph construction from unstructured sources.
- Responsible\-AI practices — bias evaluation, red\-teaming, OWASP Top 10 for LLMs, prompt injection defense, NIST AI RMF, ISO/IEC 42001\.
- Container experience (Docker, Kubernetes) and event\-driven architecture experience.
- Experience supporting third\-party audits — evidence collection and auditor\-facing documentation.
- Open\-source contributions, technical writing, conference talks, or research publications in AI/ML are a strong plus.
- Advanced degree (MS or PhD) in CS, ML, Statistics, or a related quantitative field is preferred.
WHY JOIN US
At Gene by Gene, you’ll join a mission\-driven team advancing the science of genetics and discovery. You’ll have the opportunity to shape meaningful campaigns, tell compelling brand stories, and collaborate with talented professionals who share your passion for creativity, curiosity, and impact.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Gene by Gene, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Gene by Gene AI Hiring
Gene by Gene has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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