Machine Learning Engineer Jobs in Georgia

INTRODUCTION

Iterable is the leading AI-powered customer engagement platform that helps leading brands like Redfin, SeatGeek, Priceline, Calm, and Box create dynamic, individualized experiences at scale. Our platform empowers organizations to activate customer data, design seamless cross-channel interactions, and optimize engagement—all with enterprise-grade security and compliance. Today, nearly 1,200 brands across 50+ countries rely on Iterable to drive growth, deepen customer relationships, and deliver joyful customer experiences. Our success is powered by extraordinary people who bring our core values—Be an Owner, Growth Mindset, Run as One, Transparency—to life. We foster a culture of innovation, collaboration, and inclusion, where ideas are valued and individuals are empowered to do their best work. That’s why we’ve been recognized as one of Inc’s Best Workplaces and Fastest Growing Companies, and were recognized on Forbes’ list of America’s Best Startup Employers in 2022. Notably, Iterable has also been listed on Wealthfront’s Career Launching Companies List and has held a top 10 ranking on the Top 25 Companies Where Women Want to Work. With a global presence—including offices in San Francisco, New York, Denver, London, and Lisbon, plus remote employees worldwide—we are committed to building a diverse and inclusive workplace. We welcome candidates from all backgrounds and encourage you to apply. Let’s shape the future of customer engagement together!

Position Overview:

We are looking for a Senior Machine Learning Engineer to build the core Machine Learning foundations that power Nova’s agentic experiences. This role focuses on applied Machine Learning in production environments: retrieval systems, evaluation frameworks, and model integration layers that make AI features reliable, scalable, and repeatable. You will design and implement the underlying components that support rich, intelligent interactions in the Iterable platform. You will work closely with backend, frontend, and product teams to shape how Machine Learning is introduced and maintained across the company. The work blends hands-on engineering with system design, and is ideal for someone who can drive complex efforts independently, make practical architectural decisions, and collaborate in a fast-moving, cross-functional product environment.

Responsibilities:

- Design and build Machine Learning platform components that support agentic systems, including retrieval pipelines, indexing strategies, and model integration layers.
- Introduce and operationalize RAG use cases, from data sourcing and embedding generation to runtime retrieval patterns.
- Develop generalized evaluation frameworks for LLM- and agent-based features, including offline metrics, golden datasets, and continuous monitoring.
- Implement abstractions, tooling, and reusable patterns that enable other teams to build ML- and LLM-powered experiences efficiently.
- Partner with backend engineers to productionize ML features with strong reliability, observability, and performance characteristics.
- Prototype applied ML solutions to validate feasibility before investing in full builds.
- Ensure secure, robust handling of data used in ML workflows and retrieval operations.
- Collaborate with product, design, and engineering to align ML system design with user experience and product goals.
- Contribute to iterative improvements of the Nova agent framework, including workflows built with Mastra and TypeScript.

Qualifications

- 5+ years experience as a Machine Learning Engineer or similar role focused on production systems.
- Strong engineering skills with Python or TypeScript, including experience building ML workflows in frameworks like Mastra or comparable agent/LLM toolkits.
- Experience with retrieval systems, vector databases, search technologies, or RAG architectures.
- Prior work integrating ML or LLM-powered features into production applications.
- Understanding of ML evaluation techniques, experimentation design, and failure analysis.
- Ability to lead complex projects, make practical trade-offs, and work independently in areas of ambiguity.
- Strong communication and collaboration skills in a distributed environment.

Bonus Points

- Experience building ML or LLM platforms, tooling, or developer-facing frameworks.
- Prior work with embeddings, search–ranking systems, or advanced RAG architectures.
- Familiarity with event-driven systems or streaming architectures.
- Experience with model observability, performance monitoring, or proactive regression detection.
- Background in personalization, recommendations, or applied NLP.
- Experience working in remote-first engineering teams.

Perks & Benefits:

- Competitive salaries, meaningful equity, & 401(k) plan
- Medical, dental, vision, & life insurance
- Balance Days (additional paid holidays)
- Fertility & Adoption Assistance
- Paid Sabbatical
- Flexible PTO
- Monthly Employee Wellness allowance
- Monthly Professional Development allowance
- Pre-tax commuter benefits
- Complete laptop workstation

The US base salary range for this position at the start of employment is $133,500 - $212,000. Within this range, individual pay is determined by specific US work location, as well as additional factors, including job-related skills, experience, relevant education or training, and internal equity considerations. Please note that the range listed above reflects only base salary. The total compensation package includes variable pay (where applicable), equity, plus a range of benefits, including medical, dental, vision, and financial. In addition, we offer perks such as generous stipends for health & fitness and learning & development, among others.

Recruitment Disclaimer:

Please be aware that Iterable, Inc. (“Iterable”) and our official professional recruiting agencies and platforms do not:
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- Request money, fees, or payment of any kind from prospective candidates to apply to Iterable, for employment, or for the recruitment process (e.g. for home office supplies, or training, etc.).
- Request or require personal documents like bank account details, tax forms, or credit card information as part of the recruitment process prior to the candidate signing an engagement letter or an employment contract with Iterable.

You may see all job vacancies on our official Iterable channels:
- Official Iterable website, Careers page
- Official LinkedIn Jobs page

Iterable is not affiliated in any way to these impostors and we hereby confirm that such individuals/entities are not authorized, encouraged, or sponsored to act on behalf of Iterable. Such job opportunities are entirely fake and not valid. Therefore, please disregard any written or oral request for a job offer or an interview that you believe is or might be fraudulent or suspicious and immediately reach out to us via email at talent-ops@iterable.com upon receiving a suspicious job offer. Criminal and/or civil liabilities may arise from such actions, and Iterable expressly reserves the right to take legal action, including criminal action, against such individuals/entities whenever such phenomena occur. In any case, please note that under no circumstances shall Iterable and any of its affiliates be held liable or responsible for any claims, losses, damages, expenses or other inconvenience resulting from or in any way connected to the actions of these impostors.

Iterable is an Equal Employment Opportunity employer that proudly pursues and hires a diverse workforce. Iterable does not make hiring or employment decisions on the basis of race, color, religion or religious belief, ethnic or national origin, nationality, sex, gender, gender-identity, sexual orientation, disability, age, military or veteran status, or any other basis protected by applicable local, state, or federal laws or prohibited by Company policy. Iterable also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. Pursuant to the San Francisco Fair Chance Ordinance and other similar state laws and local ordinances, and its internal policy, Iterable will also consider for employment qualified applicants with arrest and conviction records.