ML Engineering Manager

Harnham; Chicago (IL); Hybrid; USD 220,000 - 240,000; Full time

Benefits offered by this job

Annual bonus

Equity opportunities

Health benefits

Hybrid work

Job summary

Harnham is seeking a senior ML Engineering Manager to lead a high-performing team of machine learning engineers and applied scientists in Chicago. You will shape roadmaps, guide production ML deployments, and drive experimentation and rigorous evaluation to deliver measurable business impact.

The role requires 5+ years in ML/Data Science and 2+ years of people management, with strong communication, collaboration with business stakeholders, and a track record of delivering complex models in risk

Qualifications

• 5+ years of experience in Machine Learning, Data Science, Applied AI, or related software engineering disciplines.
• At least 2 years of people management experience (3+ direct reports).
• Strong technical depth in ML, statistics, software engineering, or related fields.
• Experience deploying production ML models in large-scale environments, e.g., credit risk or fraud.
• Proven ability to lead in ambiguity and data-driven decision making.

Responsibilities

• Lead, mentor, and develop a team of ML engineers and scientists.
• Shape the ML roadmap and oversee model development lifecycle from training to production.
• Collaborate with business and technical stakeholders to translate model results into impact.
• Drive experiments, evaluate results rigorously, and prioritize resources.

Skills

Machine Learning

Applied AI

Data Science

Team Leadership

Technical Interviewing

Model Evaluation

Bayesian Statistics

Causal Inference

Experimentation

Stakeholder Communication

Tools

Python

Job description

$220,000-$240,000 base + annual bonus + equity

THE COMPANY

Join a high-growth technology company at the forefront of machine learning and risk intelligence. The organization develops advanced AI and machine learning solutions that power critical decision‑making at scale, helping businesses manage risk, improve outcomes, and unlock new opportunities.

This is an opportunity to lead a highly technical team operating at the intersection of machine learning, software engineering, experimentation, and applied statistics. The environment is collaborative, research-driven, and focused on delivering measurable business impact through data-informed innovation.

RESPONSIBILITIES

Lead and Develop a High-Performing Team

• Lead, mentor, and develop a team of machine learning engineers and applied scientists, supporting career growth and technical excellence.
• Foster a culture of experimentation, knowledge sharing, continuous learning, and constructive feedback.
• Conduct regular 1:1s, performance discussions, and coaching to help team members achieve their professional goals.
• Drive hiring efforts, participate in technical interviews, and help maintain a high talent bar as the team scales.
• Identify opportunities to improve team processes, collaboration, and delivery effectiveness.

Drive Machine Learning Innovation and Delivery

• Partner with technical leads to shape and refine the roadmap for machine learning initiatives and model improvements.
• Oversee a portfolio of experiments, balancing near‑term business objectives with longer‑term research and innovation opportunities.
• Apply rigorous evaluation frameworks to ensure model improvements translate successfully from offline testing to production environments.
• Make informed prioritization decisions regarding resource allocation, experimentation, and delivery timelines.
• Guide teams through the full lifecycle of model development, validation, deployment, and release.

Partner with Business and Technical Stakeholders

• Collaborate closely with business stakeholders to define, measure, and deliver against performance objectives.
• Work with platform and infrastructure teams to enhance experimentation capabilities, training pipelines, and ML tooling.
• Translate complex technical concepts and model performance metrics into clear business impact and strategic recommendations.
• Present results, insights, and recommendations to senior leadership and cross‑functional stakeholders.

SKILLS AND EXPERIENCE

• 5+ years of experience in Machine Learning, Data Science, Applied AI, or related software engineering disciplines.
• At least 2 years of people management experience (3+ direct reports), including coaching, performance management, and team development; owning team KPIs
• Strong technical depth in machine learning, applied statistics, software engineering, or a combination of these disciplines.
• Experience evaluating and deploying production ML models in large‑scale environments, ideally in credit risk, fraud detection, payment processing or similar.
• Proven ability to lead teams through ambiguity, experimentation, and data‑driven decision‑making.
• Strong understanding of model performance evaluation, experimentation methodologies, and specifically with statistical rigor – Bayesian, causal inference, etc.
• Excellent written and verbal communication skills, with the ability to influence both technical and non‑technical audiences.
• Demonstrated ability to balance research initiatives with delivery commitments in a fast‑paced environment.
• Passion for engineering quality, reliability, reproducibility, and operational excellence.

BENEFITS

• Competitive compensation package.
• Annual bonus.
• Equity participation opportunities.
• Comprehensive health and wellness benefits.
• Flexible and hybrid working arrangements.
• Professional development and learning opportunities.
• Opportunity to work on cutting‑edge machine learning challenges with significant business impact.

KEY TERMS

ML Engineering Manager | Machine Learning Manager | AI Engineering | Data Science Leadership | Applied Machine Learning | MLOps | Experimentation | Statistical Modeling | Team Leadership | Risk Analytics | Predictive Modeling | Production ML | Engineering Management | AI Research | Machine Learning Infrastructure | Python | Model Evaluation | Distributed Teams | Technical Leadership | Data‑Driven Decision Making | Bayesian Statistics | Causal Inference | Fraud Detection | Anomaly Detection | Credit Risk | Payment Processing