Machine Learning Lead Engineer

Cox Enterprises•Atlanta, GA

1d•$134,900 - $224,900•Hybrid

About The Position

Cox Automotive is hiring a Machine Learning Engineer Lead for the AI Accelerator team. This role involves building and scaling machine learning models across the company, from design through production, with expertise in areas like deep learning, generative AI, computer vision, optimization, or causal machine learning. The Lead will also define the team's model selection strategy and manage AI governance, including bias checks, compliance tracking, and audit trails. Additionally, the Lead will develop evaluation systems to assess AI agent and model performance using the GenAI Eval Framework.

Requirements

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.
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.
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.

Nice To Haves

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.

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.
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.
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.
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.

Benefits

health care insurance (medical, dental, vision)
retirement planning (401(k))
paid days off (sick leave, parental leave, flexible vacation/wellness days, and/or PTO)
flexible vacation with pay
seven paid holidays
up to 160 hours of paid wellness annually
bereavement leave
time off to vote
jury duty leave
volunteer time off
military leave
parental leave