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About This Role
At Ubiety Technologies, Inc we are redefining physical security. We develop innovative device detection and RF signaling products that protect homes, businesses, and communities. As part of our fast-moving, highly collaborative startup team, you'll work alongside an exceptional team of data scientists, engineers, and creative designers to invent and deliver transformative products from inception to production.
We are proud of our culture of innovation, speed, ownership, and accountability. We value agility and embrace ambiguity so we can move quickly, iterate effectively, and outperform traditional approaches. Every employee is an equity owner in the company and we expect our team members to think and act like an owner would: understand what matters most, prioritize accordingly, identify problems before they become blockers, and take responsibility for driving solutions forward. As a startup, focus, ingenuity, and speed combine to create our competitive advantage.
The Senior Data Science Manager leads the vision, architecture, and execution of Ubiety’s data science initiatives. They oversee the data science team and drive the development of advanced machine learning models and predictive analytics while aligning technical output with company-wide strategic objectives. The right candidate will be both a stellar people leader and technical contributor.
This is a player-coach role. The right candidate is equally comfortable setting technical direction, developing talent, partnering with executives, reviewing architecture, digging into data, writing or reviewing code, and stepping directly into a difficult technical problem when that's what the business needs.
We are looking for someone with a strong bias for action who knows how to distinguish what is important from what is merely interesting. You should be energized by complex technical problems and the opportunity to build capabilities that have not existed before!
If you're motivated by building, experimenting, solving difficult problems, and seeing your work translate into measurable outcomes, we want to hear from you!
Responsibilities
As a key technical leader, this role will:
- Lead, mentor, and develop a high-performing team of data scientists while fostering a culture of technical excellence, accountability, curiosity, and continuous improvement.
- Serve as a hands-on technical leader who can move fluidly between strategy and execution, stepping directly into modeling, analysis, architecture, experimentation, or troubleshooting when needed.
- Establish the strategic roadmap and measurable milestones for data science initiatives, ensuring resources are concentrated on the opportunities with the greatest potential business and product impact.
- Translate broad or ambiguous business problems into clear hypotheses, technical approaches, experiments, and measurable outcomes.
- Partner closely with executive leadership, product, engineering, and other teams to identify and prioritize high-impact opportunities for machine learning, predictive analytics, and data-driven decision-making.
- Lead the development, validation, deployment, and continuous improvement of scalable machine learning models and analytical systems.
- Make thoughtful technical and product decisions with incomplete information, balancing speed, quality, scalability, and business impact.
- Build an experimentation culture that encourages calculated risk-taking, rapid learning, and iteration rather than waiting for perfect information.
- Continuously look for ways to accomplish more with available resources through better architecture, automation, tooling, prioritization, and creative problem-solving.
- Establish clear success measures before work begins and use data to determine whether initiatives are delivering the intended impact.
- Identify risks, dependencies, and blockers early and take ownership of driving them toward resolution.
- Drive hiring and onboarding efforts, identifying talent and capability gaps and scaling the team thoughtfully as business needs evolve.
- Champion strong practices in data governance, experimental design, model validation, observability, documentation, and code quality.
- Ensure technical solutions ultimately translate into usable products, better decisions, improved customer outcomes, or measurable business value.
Technical Skills and Abilities
- Deep expertise in machine learning, statistical modeling, predictive analytics, and experimental design.
- Strong proficiency in Python, including tools such as NumPy, Pandas, Scikit-learn, PyTorch and/or TensorFlow.
- Advanced SQL skills and demonstrated ability to work directly with large and complex datasets.
- Experience designing, deploying, monitoring, and improving machine learning models in production environments.
- Proven experience with cloud-native data and machine learning infrastructure such as AWS or GCP and technologies such as BigQuery, Snowflake, and Airflow.
- Strong understanding of data engineering principles, streaming architectures, data quality, and their impact on machine learning pipelines.
- Ability to evaluate emerging tools, techniques, and technologies quickly and determine where experimentation is warranted.
- Exceptional ability to synthesize complex technical findings and communicate them clearly to both technical and non-technical stakeholders.
Requirements
- BS or MS degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field, or equivalent practical experience.
- 7+ years of progressive experience in data science, machine learning, applied statistics, or a related discipline.
- 3+ years of experience leading, managing, or developing technical teams.
- Demonstrated record of taking complex data science or machine learning initiatives from concept and experimentation through production and measurable business impact.
- Strong experience hiring, developing, coaching, and retaining high-performing technical talent.
- Demonstrated ability to operate effectively in environments where priorities evolve, information is incomplete, and resources are constrained.
- Strong ownership mentality with a record of proactively identifying problems, driving decisions, and following through to results.
- Ability to prioritize aggressively, focus teams on the highest-value work, and say no or not yet to lower-impact opportunities.
- Comfortable challenging assumptions, testing new approaches, learning from unsuccessful experiments, and adjusting quickly.
- Low-ego, hands-on leadership style with a willingness to do the work required to help the team succeed.
Preferred
- Working knowledge of technologies such as Kafka, Flink, Spark Structured Streaming, or other real-time and streaming data architectures.
- Experience building data science or machine learning capabilities within a startup, high-growth technology company, or similarly resource-constrained environment.
- Experience working with device, RF, IoT, telecommunications, location, behavioral, or other high-volume signal data is a plus.
Key Success Metrics
Business Impact
Data science initiatives produce measurable improvements in company, customer, product, or operational outcomes.
Speed to Value
The team moves effectively from hypothesis to experiment to production, delivering meaningful learning and measurable progress quickly.
Prioritization & Focus
Team resources are consistently concentrated on the highest-impact opportunities, with clear tradeoffs and minimal effort spent on work that does not advance company objectives.
Production Performance
Machine learning models and analytical systems are reliable, scalable, observable, and continuously improved based on real-world performance.
Team Performance & Development
Data scientists operate with increasing autonomy, technical depth, ownership, and effectiveness as a result of strong coaching and leadership.
Cross-Functional Impact
Data science operates as an effective partner to product, engineering, and company leadership and translates technical capabilities into actionable business and product value.
Innovation & Learning Velocity
The team consistently tests new ideas, technologies, and approaches, quickly distinguishing promising opportunities from those that should be abandoned or deprioritized.
Ownership & Execution
Risks and blockers are surfaced early, commitments are followed through, and the team consistently converts decisions into action and action into measurable results.
Ubiety Technologies, Inc is an Equal Opportunity Employer. We celebrate diversity in all forms and are committed to maintaining a discrimination-free workplace that treats applicants and employees with dignity and respect. Our employment process is conducted without regard to race, color, religion, nationality or ethnic background, sex, pregnancy, sexual orientation, gender identity or expression, age, disability, protected veteran status, genetic information, or other attributes protected by state, federal, and local law.
Salary Context
This $156K-$212K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Ubiety, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($184K) sits 13% below the category median. Disclosed range: $156K to $212K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Ubiety AI Hiring
Ubiety has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $212K - $212K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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