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About This Role
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:
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- 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
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 Sightly Enterprises, Inc., 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.
Sightly Enterprises, Inc. AI Hiring
Sightly Enterprises, Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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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