Senior Machine Learning Engineer

About the role

You will architect and deploy machine learning systems that power next generation personalization for millions of customers across the world. This role owns the end to end lifecycle of production grade models, from discovering signals in behavioral data to validating lift in real world engagement. You will collaborate closely with product managers and data scientists to translate ambiguous business goals into precise technical specifications and measurable success criteria. A core part of this position is building robust feature stores and experiment frameworks that enable rapid iteration while maintaining strict reliability standards. You will mentor junior engineers and data scientists on best practices for model evaluation, debugging, and scalable deployment. The work requires balancing statistical rigor with engineering constraints to ensure models remain performant and maintainable at scale. You will continuously monitor production systems, diagnose regressions, and drive improvements that compound over time. Finally, you will partner with cross functional stakeholders to ensure machine learning initiatives directly support key business objectives and customer outcomes.

Key facts

What you'll do

Design and implement machine learning pipelines that process high volume behavioral data to generate actionable customer insights.

Build and maintain feature infrastructure that ensures consistency between training and serving environments while optimizing for latency and throughput.

Partner with data scientists to iterate on model architectures, evaluate performance tradeoffs, and select approaches that balance accuracy with scalability.

Own the deployment and monitoring of models in production, creating observability tools that surface anomalies and performance degradation quickly.

Lead experiments that measure the impact of model changes on customer engagement, retention, and conversion across multiple channels.

Collaborate with product teams to define experimentation roadmaps, establish evaluation metrics, and interpret results for business stakeholders.

Champion software engineering best practices such as modular design, comprehensive testing, and code review to keep the ML codebase healthy and maintainable.

Work with data infrastructure teams to ensure data quality, lineage, and privacy compliance across machine learning workflows.

Drive technical discovery for new capabilities, researching and prototyping approaches that address emerging customer engagement challenges.

Mentor engineers and analysts on machine learning concepts, tooling, and debugging techniques to raise the overall technical capability of the organization.

Translate ambiguous product requirements into concrete analytical plans and machine learning solutions that deliver measurable business value.

Contribute to architectural decisions that affect data platforms, model serving infrastructure, and long term scalability of personalization systems.

Requirements

US citizens and permanent residents only.

Strong proficiency in Python and experience building production grade machine learning systems.

Solid understanding of statistical and machine learning models such as regression, classification, and sequence modeling.

Experience with data processing frameworks and SQL for extracting, transforming, and joining large scale datasets.

Familiarity with cloud based model training and deployment platforms used for scalable machine learning workflows.

Demonstrated ability to write clean, tested code that integrates into large, distributed software systems.

Excellent communication skills to explain complex model behavior to both technical and non technical audiences.

Track record of owning end to end projects, from problem definition through deployment and post launch measurement.

Ability to work effectively in an agile environment, participating in sprint planning, code reviews, and iterative delivery.

Nice to have

Experience with experiment design and causal inference methods for measuring impact of model changes.

Knowledge of deep learning approaches for sequential behavior or natural language processing in customer contexts.

Background in building or operating recommendation, ranking, or propensity models at scale.

Familiarity with MLOps tooling, monitoring frameworks, and data versioning practices.

Practical notes

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Machine learning engineers build statistical foundations that enable personalized customer interactions. Common tools include Python, SQL, and cloud-based model training platforms. The role balances model accuracy with system performance at scale. Cross functional communication aligns technical work with business outcomes. Continuous experimentation measures the impact of model improvements on customer behavior.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.

About the company

pages. Let's shape the future of customer engagement together! The Senior Technical Support Specialist role offers an exciting opportunity for individuals with a passion for learning, problem-solving, and technology.