Machine Learning Engineer

Role Description

Elevate's data and insights products are among the company's most important product bets. They help sports, entertainment, and brand clients understand audiences, build personas, generate research-style outputs, and turn complex data into action. The product has real momentum, but the next stage depends on making the data more trustworthy, the AI outputs more useful, and the product experience easier for both internal teams and external clients to use.

As Machine Learning Engineer for data and insights products, you will own the practical ML and data science layer behind that work. This is a hands-on role for someone who can move from messy data and ambiguous business questions to production-ready models, validation workflows, and measurable product improvements. You will partner with product, engineering, analytics, and business stakeholders to make sure our AI and ML work is grounded in real data and tied to business outcomes.

This role is not a research-only data science role and it is not a prompt-only AI role. We need someone who can build, test, explain, and ship.

Qualifications

• 3+ years of experience in machine learning, data science, applied AI, or a closely related role.
• Strong Python and SQL skills, with experience working across large, imperfect, real-world datasets.
• Experience building models or analytical systems that were evaluated against business outcomes, not just offline accuracy.
• Practical experience with data validation, anomaly detection, model evaluation, experimentation, and measurement.
• Comfort owning the full ML lifecycle: exploration, feature/data preparation, model selection, training, validation, deployment support, monitoring, and iteration.
• Ability to partner with backend and full-stack engineers to turn models and analytical workflows into product experiences.
• Ability to explain technical trade-offs to product, executive, and business stakeholders without hiding behind jargon.
• Strong product judgment: you know when ML is the right tool, when simpler rules or analysis are better, and how to push back constructively.
• A bias toward pragmatic delivery. You can start with a useful MVP, measure it, and improve it over time.

Requirements

• Customer segmentation, survey/polling data, marketing analytics, identity resolution, or sample weighting.
• AI-assisted persona generation, report insight generation, synthetic research workflows, or LLM-backed analyst tools.
• AWS data and ML services such as S3, Lambda, Glue, Redshift, SageMaker, Bedrock, or similar cloud platforms.
• Experience with data products used by non-technical business users.
• Experience in sports, entertainment, ticketing, media, sponsorship, or consumer brand analytics.

Benefits

• This position is fully remote.
• Full Time – Exempt.
• Anticipated Base Salary: $100,000 - $155,000 (this position is bonus eligible).
• Medical, Dental, Vision, Life, Short-Term & Long-Term Disability Insurance + FSA, HSA, and more.
• 401k Employer Match after meeting eligibility requirements.
• 14 Paid Holidays.
• Unlimited PTO.
• Paid Parental Leave.

Your Journey: First 90 Days

• First 30 Days:
  • Learn the product ecosystem, data sources, existing AI/ML workflows, and current pain points.
  • Meet the internal users and stakeholders who rely on these products today.
  • Understand the current persona, report insight, audience-building, synthetic research, and data-validation workflows.
  • Identify the highest-impact trust or quality issues that can be measured and improved.
• Days 31-60:
  • Own a focused improvement area such as data anomaly detection, persona quality evaluation, output validation, or synthetic research quality.
  • Define the metrics that show whether the improvement is working.
  • Partner with engineering to create a clear implementation path and ship an initial improvement.
  • Create a practical ML roadmap that separates immediate fixes from longer-term product differentiation.
• Days 61-90:
  • Deliver a visible product improvement that increases trust, usability, or client-facing value.
  • Establish a repeatable process for evaluating and improving AI/ML outputs.
  • Help leadership understand what the product area can realistically support in Q3 and what needs more investment.
  • Become the team's go-to technical owner for data and insights ML and data science questions.

Why This Role Matters Now

The product area has the potential to become more than a reporting tool. It can become an intelligence layer that helps clients understand audiences, test ideas, and make better commercial decisions. To get there, the product needs cleaner data, better validation, and AI/ML work that is grounded in reality. This role gives Elevate the in-house expertise to make that happen.

The Technology You'll Work With

• Python and SQL
• Modern data pipelines and cloud data platforms
• AWS services across data, backend, and ML workflows
• AI and LLM-backed product workflows
• Data and insights frontend and backend systems
• Partner data sources and audience datasets

Our Product Engineering Principles

• Customer Obsession: We start with the customer and work backward. We don't build features; we solve customer and business problems.
• Ownership: Teams own outcomes, not just outputs. Success means business impact and customer delight, not shipping on schedule.
• Simplicity: We choose architectural and product simplicity over complexity. Complex solutions are harder to maintain, evolve, and explain.
• Data-Driven Decisions: We measure what matters and use data to guide our product development and business decisions.
• Rapid Learning: We implement quick feedback loops, focus on learning through experimentation, and value progress over perfection.
• Transparency: We invite visibility and are transparent with where we're spending our time.