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About This Role
Basis:
Permanent \- Full\-time
Area of Interest:
Data \& Analytics
Location:
Atlanta, Georgia
Who we are:
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Geotab ® is a global leader in IoT and connected transportation and certified “Great Place to Work™.” We are a company of diverse and talented individuals who work together to help businesses grow and succeed, and increase the safety and sustainability of our communities.
Geotab is advancing security, connecting commercial vehicles to the internet and providing web\-based analytics to help customers better manage their fleets. Geotab’s open platform and Geotab Marketplace ®, offering hundreds of third\-party solution options, allows both small and large businesses to automate operations by integrating vehicle data with their other data assets. Processing billions of data points a day, Geotab leverages data analytics and machine learning to improve productivity, optimize fleets through the reduction of fuel consumption, enhance driver safety and achieve strong compliance to regulatory changes.
Our team is growing and we’re looking for people who follow their passion, think differently and want to make an impact. Ours is a fast paced, ever changing environment. Geotabbers accept that challenge and are willing to take on new tasks and activities \- ones that may not always be described in the initial job description. Join us for a fulfilling career with opportunities to innovate, great benefits, and our fun and inclusive work culture. Reach your full potential with Geotab. To see what it’s like to be a Geotabber, check out our blog and follow us @InsideGeotab on Instagram.Who you are:
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We are always looking for amazing talent who can contribute to our growth and deliver results! Geotab is seeking a Senior Data Platform Developer who will be building the Geotab internal machine learning and generative AI platform. This role allows all Geotab data scientists and generative AI developers to explore, train, and build machine learning models, gain access to underlying Large Language Models, and build GenAI agent applications for internal services and end customers in production. If you love technology, and are keen to join an industry leader — we would love to hear from you!
What you'll do:
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As a Senior Data Platform Developer, your key area of responsibility will be developing and maintaining new machine learning platforms and managing generative AI applications and agents. You will be responsible for implementing logging, monitoring, and alerting services to ensure the health of Geotab’s AI platform infrastructure, as well as enriching big data with telematics data at scale. You will need to work closely with Geotab’s data scientists and internal teams to understand data processing needs and assist with data integration for newly developed AI platforms. To be successful in this role, you will be a self\-starter who is keen to join an industry leader and enjoys working with complex, technical AI concepts.
How you'll make an impact
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- Develop and maintain new machine learning platforms managing the machine learning models and generative AI applications and agents.
- Develop processes and implement logging, monitoring, and alerting services to ensure the health of Geotab’s AI platform infrastructure.
- Develop processes to enrich Geotab’s big data with telematics data at scale.
- Work with data scientists to understand data processing needs and develop infrastructure solutions to support these initiatives.
- Create and maintain documentation for architecture, requirements, and process flows.
- Support internal Geotab teams to assist with data integration with newly developed AI platforms.
What you'll bring to the role
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- Post\-secondary Degree specialization in Computer Science, Software or Computer Engineering or a related field.
- 3\-5 years experience in a software developer or a similar role.
- 3\-5 years experience in developing production\-level systems using Python 3\.
- 3\-5 years experience in developing and maintaining production services in one of the mainstream cloud providers such as GCP, AWS, or Azure.
- 1\-5 years experience in designing, building and maintaining production\-level application containerization, such as Docker, Kubernetes, or OpenShift.
- Knowledge of fine\-tuning and self\-hosting vision large language models and generative AI related frameworks (Langgraph, CrewAI, LiteLLM) is a big plus.
- Experience with OpenSpec, Ralph, Agent Sandbox, and Sourcegraph is highly preferred.
- Knowledge of AI/ML platforms, such as Ray, VertexAI, Bedrock, and familiarity with Big Data environments (e.g. Google BigQuery).
If you got this far, we hope you're feeling excited about this role! Even if you don't feel you meet every single requirement, we still encourage you to apply.
Please note: Geotab does not accept agency resumes and is not responsible for any fees related to unsolicited resumes. Please do not forward resumes to Geotab employees.
This posting is for an existing vacancy.
Why job seekers choose Geotab
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Flex working arrangements
Home office reimbursement program
Baby bonus \& parental leave top up program
Online learning and networking opportunities
Electric vehicle purchase incentive program
Competitive medical and dental benefits
Retirement savings program
- The above are offered to full\-time permanent employees only
How we work
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At Geotab, we have adopted a flexible hybrid working model in that we have systems, functions, programs and policies in place to support both in\-person and virtual work. However, you are welcomed and encouraged to come into our beautiful, safe, clean offices as often as you like. When working from home, you are required to have a reliable internet connection with at least 50mb DL/10mb UL. Virtual work is supported with cloud\-based applications, collaboration tools and asynchronous working. The health and safety of employees are a top priority. We encourage work\-life balance and keep the Geotab culture going strong with online social events, chat rooms and gatherings. Join us and help reshape the future of technology!
We believe that ensuring diversity is fundamental to our future growth and progress and is an integral part of our business. We believe that success happens where new ideas can flourish – in an environment that is rich in diversity and a place where people from various backgrounds can work together. Geotab encourages applications from all qualified individuals. We are committed to accommodating people with disabilities during the recruitment and assessment processes and when people are hired. We will ensure the accessibility needs of employees with disabilities are taken into account as part of performance management, career development, training and redeployment processes. If you require accommodation at any stage of the application process or want more information about our diversity and inclusion as well as accommodation policies and practices, please contact us at [email protected]. By submitting a job application to Geotab Inc. or its affiliates and subsidiaries (collectively, “Geotab”), you acknowledge Geotab’s collection, use and disclosure of your personal data in accordance with our Privacy Policy.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Geotab, 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 $214,900 based on 6,420 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 $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Geotab AI Hiring
Geotab has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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