Machine Learning Engineering Lead

Sightly is a growing technology company leading the revolution in real-time marketing and brand intelligence. Join us as we pursue our disruptive mission to empower businesses everywhere to make the most authentic + profitable decisions in real time.

Our AI-driven Brand Mentality® platform enables brands and agencies to leverage an ever-changing ocean of news, premium publisher, CTV, social, creator, and audience data to make more intelligent decisions at the speed of culture. At Sightly, we’re passionate about our product, our customers, our impact on the world, and most importantly our team.

What you’ll work on:

Enrichment models across our cultural data pipeline: entity extraction, topic and stance classification, embeddings, clustering, sentiment, brand safety, and related tasks across billions of news and social records

Multi-modal enrichment for image and video signals from social platforms, complementing our text-heavy core

Ad optimization systems built from the ground up, including bid optimization, budget allocation, creative selection, audience targeting, or related problems, grounded in historical performance data and well-reasoned heuristics

Experimentation design and execution: framing the question, choosing the right test, instrumenting it, and producing results the business can act on

Production ML infrastructure on GCP: training, evaluation, deployment, monitoring, and the glue that keeps models reliable as data shifts

Technical leadership for a small ML team, including code review, mentorship, prioritization, and raising the bar on rigor without slowing delivery

Cross-functional partnership with Data Engineering on pipeline integration, and with Account Management and Performance Managers to translate business problems into model problems

Core Competencies:

ML Breadth & Depth

Strong foundation across classical ML, neural networks, and Transformers, reaching for the right tool rather than the trendiest one

Comfortable with both supervised and unsupervised paradigms: classification, regression, clustering, dimensionality reduction, representation learning

Practical fluency with NLP and at least working familiarity with computer vision for image and video enrichment

Understanding of when a simple model beats a complex one, and the discipline to ship the simple one

Experimentation & Research Rigor

Track record of structuring and running experiments end-to-end: hypothesis, design, instrumentation, analysis, decision

Comfortable with ad hoc statistical testing, picking the right test for the task, reasoning about power, controlling for confounds

Knows the difference between a model that benchmarks well offline and one that holds up in production

Research mindset paired with a shipping mindset: rigorous, but allergic to research-for-its-own-sake

Optimization

Experience building optimization systems, whether mathematical optimization, heuristics, or learned policies, applied to a real-world domain

Comfortable reasoning about objective functions, constraints, and tradeoffs in messy business contexts

Advertising or adtech optimization experience is a strong plus

Production ML Engineering

Strong Python and the standard ML stack: scikit-learn, PyTorch, TensorFlow, HuggingFace, NumPy, pandas

FastAPI and async/await patterns for serving models and building ML-facing services

Experience working with data at scale, including the practical realities of billions of records: partitioning, sampling, distributed processing, cost management

GCP for training, serving, and infrastructure, such as Vertex AI, Cloud Run, GCS, or equivalent

PostgreSQL and Snowflake for working with large-scale data

Docker and CI/CD pipelines for reproducible, deployable ML workloads

Comfortable with the realities of production ML: data drift, retraining cadence, monitoring, cost management

Leadership & Collaboration

Experience leading or mentoring engineers, even informally, through code review, technical direction, and raising the bar on quality

Strong collaboration habits with Data Engineering, and the ability to translate fluently between technical and business audiences

Can sit with an Account Manager or Performance Manager, understand what they actually need, and turn it into a tractable modeling problem

Software Engineering Fundamentals

Clean code habits, sensible architecture, strong typing discipline

Test-driven mindset for ML code: covering data assumptions, edge cases, and regression paths, not just happy paths

Comfortable with modern dev practices: Git, code review, CI/CD

Nice to have:

Advertising, adtech, or media industry experience

Familiarity with LLMs and modern AI tooling, useful context for the broader engineering org but not the focus of this role

Causal inference or uplift modeling background

Experience with recommendation systems or ranking

5+ years of ML experience, ideally with a foundation built before the LLM era

Location:

This is a remote position as we are a 100% distributed company