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About This Role
About ScienceLogic…
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ScienceLogic is redefining IT operations for the modern enterprise. Our AIOps platform empowers organizations to achieve Autonomic IT — where systems are self\-healing, self\-optimizing, and seamlessly aligned with business outcomes. We help enterprises and service providers gain unified visibility across hybrid and multi\-cloud environments, automate workflows, and unlock performance at scale.
We’re accelerating digital transformation through the power of automation, AI, and analytics — giving IT and business leaders the tools to deliver superior customer experiences, drive efficiency, and innovate with confidence.
ScienceLogic is seeking a hands\-on Director of Data Science who leads from the front—architecting solutions while coaching and empowering a high\-performing data science team. You’ll spend roughly 60% of your time hands\-on: designing models, shaping our agentic AI and ML platforms, and solving the hardest problems, and roughly 40% leading a small three person team: mentoring, hiring, performance management, and scaling the maturity of the function. You’ll partner closely with Product, Engineering, and senior leadership to align AI investments with business strategy. If you’re energized by deep technical work and growing a high\-performing team in equal measure, this role is for you.
What you’ll be doing…
Technical Leadership \& Delivery:
- Architect complex data science and agentic AI systems end to end, from foundational capabilities to production deployment.
- Personally build, validate, and deploy the highest\-complexity predictive models and machine learning solutions that solve core business problems.
- Define enterprise\-level best practices for AI/ML systems, experimentation, governance, and operationalization.
- Drive standards for scalability, observability, model lifecycle management, and responsible AI across the organization.
Cross\-Team Influence \& Architecture:
- Architect foundational AI capabilities and shared frameworks that other teams consume, setting the standards they build against.
- Influence roadmap direction through technical expertise and strategic insight, translating business needs into a coherent data science agenda.
- Lead evaluation and adoption of emerging technologies and methodologies, solving highly ambiguous problems that span multiple systems and domains.
Model Deployment \& Monitoring:
- Partner with software engineers and DevOps to deploy models into production environments.
- Monitor the performance of models over time, recalibrating and optimizing them as necessary.
- Design and implement A/B testing frameworks to evaluate model effectiveness and impact.
Team Leadership \& Growth:
- Lead, manage, and develop an established team of three data scientists, owning their growth, performance, and career trajectories.
- Set technical and cultural direction across the team and influence broader organizational capability.
- Mentor senior technical talent, raising the bar on craft, rigor, and delivery.
- Grow the team through hiring—defining roles, raising the talent bar, and scaling the function as demand increases.
- Collaborate with Product, Engineering, and leadership to align AI investments with business strategy and communicate findings to stakeholders.
Qualities you possess…
- 10–15 years of experience in data science, machine learning, or a related field, including demonstrated technical leadership across multiple teams or initiatives.
- 3–5\+ years leading and growing a data science team, with a track record of mentoring and developing senior technical talent.
- Recognized depth in machine learning, AI systems, or advanced analytics—someone the organization turns to as an expert.
- Proven track record architecting and deploying machine learning models and data science solutions into production at scale.
- Strong experience with statistical analysis, predictive modeling, and experimentation/A/B testing.
- Proficiency in Python and/or R and machine learning libraries such as scikit\-learn, TensorFlow, or PyTorch.
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- Excellent communication skills—able to translate complex technical work into strategy and recommendations for senior stakeholders.
As a plus, you have...
- Hands\-on experience building agentic AI systems, LLM applications, or deep learning and NLP solutions.
- Experience with cloud platforms (AWS, GCP, or Azure) for data science and machine learning.
- Familiarity with big data technologies like ClickHouse, Spark, Hadoop, or Databricks.
- Strong knowledge of SQL and experience with database querying.
- Familiarity with data visualization tools (e.g. Tableau, Power BI, or Matplotlib).
Benefits \& Perks
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- Comprehensive medical, dental and vision plans.
- 401(k) plan with employer match.
- Flexible Paid Time Off (FTO) so that you can take the time that you need to re\-energize.
- Volunteer Time Off (VTO) \- take two days off per calendar year to volunteer with your preferred charitable organization.
- 5\-year Service Milestone Sabbatical.
- Paid parental leave.
- Generous employee referral bonus program.
- Pet insurance.
- HQ Office centrally located in Reston Town Center featuring a well\-stocked kitchen with rotating snacks and beverages, and catered lunch on Thursdays.
- Regular virtual company\-wide events, including cooking classes, yoga, meditation and more.
- The opportunity to learn and develop from some of the best and brightest minds in the industry!
*Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. At ScienceLogic, we are dedicated to building a diverse, inclusive and authentic workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.*
*All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or any other applicable legally protected characteristics in the location in which you are applying.*
About ScienceLogic
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ScienceLogic is a leader in IT Operations Management, providing modern IT operations with actionable insights to resolve and predict problems faster in a digital, ephemeral world. Its solution sees everything across cloud and distributed architectures, contextualizes data through relationship mapping, and acts on this insight through integration and automation.
www.sciencelogic.com
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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At ScienceLogic, 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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
ScienceLogic AI Hiring
ScienceLogic has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $215,300 across 535 tracked positions. That's 7% 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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