Note: The job is a remote job and is open to candidates in USA. The company is a high-growth technology organization developing machine learning and risk intelligence solutions for critical, large-scale business decision-making. The ML Engineering Manager will lead machine learning engineers and applied scientists, oversee model development and experimentation, and align research and delivery with measurable business outcomes. The role also partners with business, platform, and infrastructure stakeholders to guide production ML deployment and communicate technical results to senior leadership.
Responsibilities
- Lead and 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
- 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
- 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
- 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
- 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.
Company Overview
Company H1B Sponsorship