Machine Learning Lead Engineer

Cox Automotive

Candler-McAfee, GA

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JOB DETAILS

SALARY

$134,900–$224,900

SKILLS

Algorithms, Amazon Web Services (AWS), Analysis Skills, Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Automotive Engineering, Benchmarking, Cloud Computing, Coaching, Communication Skills, Comparative Analysis, Competitive Analysis/Strategy, Computer Vision, Consulting, Cost Modeling, Data Science, Data Sets, Deep Learning, Documentation, Expense Reports, Hyperion Pillar, Leadership, Machine Learning, Maintain Compliance, Mentoring, Open Source, Product/Service Launch, Programming Tools, Proof of Concept, Regulations, Regulatory Compliance, Research Laboratory, Research Skills, Risk, Risk Modeling, Strategic Planning, System Integration (SI), Team Building, Team Lead/Manager, Technical Research, Testing

LOCATION

Candler-McAfee, GA

POSTED

Today

Cox Automotive is hiring a Machine Learning Engineer Lead for the AI Accelerator team. The role spans three areas. The Lead builds and scales machine learning models across the company, from design through production, and brings deep skill in one area such as deep learning, generative AI, computer vision, optimization, or causal machine learning. The Lead also sets the model selection strategy for the team and owns AI governance policy, including bias checks, compliance tracking, and audit trails. The Lead builds evaluation systems that measure whether AI agents and models perform as expected, using the GenAI Eval Framework.

WHAT YOU'LL DO:

Key Responsibilities

Design, build, and maintain ML models, algorithms, and pipelines for training, inference, and production
Use AI tools such as Claude Code to speed up coding, feature work, and testing
Build ML infrastructure, monitoring, and documentation with engineering partners
Turn model results into business value, and share progress with stakeholders across teams
Coach teams on AI adoption, and lead AI transformation work, from tool testing to rollout
Track new advances in ML and AI, and publish or present findings through papers and talks
Design agent based workflows for training, data pipelines, and analysis, matched to team skill level

Model Selection & AI Governance Strategy

Own the Model Optimization pillar of the agentic AI framework, and set the model selection strategy for the team
Pick between Claude Haiku, Sonnet, and Opus based on task complexity, cost, and speed needs
Build clear rules for when to use each model, and document the reasoning for each choice
Track model cost across projects, and report spend to leadership
Own AI governance policy, including bias checks, compliance tracking, and audit trails
Work with legal and compliance teams to meet AI regulations
Report on AI risk and model use across the AI Accelerator portfolio

Evaluation Systems (Agentic AI Framework)

Build and run evaluation systems for AI agents using the GenAI Eval Framework (GEF)
Check answer correctness, task completion, tool selection, and context quality, not just the final output
Calibrate LLM as judge scores against human baselines through agreement analysis
Curate golden datasets with human labels and short critiques for each agent type
Compare evaluation runs before and after a prompt or model change, and flag quality shifts
Build synthetic conversations to test agents before they reach production
Watch for drift, and trace the root cause when agent quality moves away from baseline
Set standards for how teams test and approve new models before rollout, and own the hardest evaluation problems as the field matures

WHO YOU ARE:

Required Skills

Skilled in AI development tools (Claude, GPT- for ML work, with the skill to check AI output before production use
Understanding of agent frameworks (AWS AgentSquad, AWS Strands, LangChain, agent patterns), from basic setup to custom enterprise design
Knowledge of AI ethics, responsible AI practice, and governance rules for business critical ML work
A steady habit of learning in AI augmented data science and responsible AI use
Skill in comparing AI models on cost, speed, and output quality, and matching each model to the task
Hands-on experience sourcing, deploying, and running open-source models in local or cloud environments
Ability to set up model serving infrastructure and get open-weight models running end to end (weights, dependencies, inference)
Skill in benchmarking open-source models against hosted/proprietary options on quality, cost, and latency to inform build-vs-buy decisions
Comfort fine-tuning, quantizing, or otherwise adapting open-source models to task and hardware constraints

Required Qualifications

Applicants must currently be authorized to work in the United States for any employer without current or future sponsorship. No OPT, CPT, STEM/OPT or visa sponsorship now or in future.
Bachelor's degree in a related field and 6 years of experience, or a master's degree and 4 years, or a Ph.D. and 1 year, or 14 years of experience with no degree
6+ years' experience working in Machine Learning focused work
Skilled in analytical thinking, consulting, requirements work, system and technology integration, and comfort with new technology
Skilled in working with intent, clear communication, building trust, driving new ideas, and pushing for high quality work
Other duties as needed
A track record of leading new projects from idea to proof of concept
Deep skill in more than one ML area and knowledge of new research
Strong background in testing technology, studying competitors, and planning strategy
Proof of sharing knowledge through papers, talks, or similar work
Experience building and leading strong research or innovation teams
Strong communication skills for both technical and executive audiences
A strong network in the ML research community
Experience with research partnerships and joint work
Must live within a commutable distance to Atlanta

Preferred Qualifications

Experience in corporate research labs, innovation teams, or technology consulting
A record of finding and rolling out breakthrough technology
Background moving research into business use
A strong name in the ML community through talks or open source work
Knowledge of new areas such as LLMs, agents, foundation models, multimodal AI, or quantum ML

Leadership Expectations

Build a culture of testing, learning, and smart risk taking
Build agreement on research priorities while keeping speed high
Grow talent through mentoring in both technical skill and research method
Share complex results and their meaning with people at every level
Lead by example through curiosity, careful method, and open sharing
Connect new research to real business use
Build the team into a known center for ML experiments

USD 134,900.00 - 224,900.00 per year

Compensation:

Compensation includes a base salary in the range of $134,900.00 - $224,900.00. The base salary may vary within the anticipated base pay range based on factors such as the ultimate location of the position and the selected candidate's knowledge, skills, and abilities. Position may be eligible for additional compensation that may include an incentive program.

Benefits:

The Company offers eligible employees the flexibility to take as much vacation with pay as they deem consistent with their duties, the company's needs, and its obligations; seven paid holidays throughout the calendar year; and up to 160 hours of paid wellness annually for their own wellness or that of family members. Employees are also eligible for additional paid time off in the form of bereavement leave, time off to vote, jury duty leave, volunteer time off, military leave, and parental leave.

EOE, including disability/vets

About the Company

Cox Automotive

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