Senior Manager, Applied AI Research

At Gen, we are scrappy and relentlessly customer driven. We create room for healthy debate, experimentation and continuous learning, and we seek out people with different experiences, identities and ideas to join our team. You’ll work with people who back each other, respect each other and understand that our differences are a competitive advantage.

If this sounds like you, we’d love you to be part of Gen.

About The Role:

As Senior Manager, Applied AI Research, you will provide hands-on technical leadership across a portfolio of applied AI initiatives that protect over 500 million users worldwide, spanning Small Language Models, reinforcement learning, agentic systems, computer vision and classical machine learning.

You will shape the architecture, modeling choices and production path for these programs while managing and growing a small team of 3-5 Research Engineers responsible for end-to-end delivery of applied AI, from problem framing and data curation through model training, customization and evaluation to deployment, monitoring and iteration at massive scale.

This role blends technical leadership with people management. You will lead the team technically by setting direction, shaping architectures and staying close to the models and code, while also growing engineers and owning delivery. You will ensure that the models your team builds reach production and contribute to Gen’s cybersecurity mission.

In This Role, You Will:

Provide hands-on technical leadership across multiple applied AI workstreams, including Small Language Models, reinforcement learning, agentic AI, computer vision and classical ML.

Drive architecture, modeling choices and productionization decisions with your team of Research Engineers.

Manage, mentor and grow a small team of 3-5 Research Engineers, including hiring, performance management, career development and day-to-day technical coaching.

Keep the team focused, unblocked and outcome-oriented.

Personally contribute to the hardest parts of the work by prototyping novel approaches, reviewing and improving model designs, and guiding training-run debugging on GPU clusters.

Set the bar for research and engineering quality across the team.

Own the full applied-AI lifecycle for your area, from problem framing and data strategy through model training, fine-tuning and evaluation to deployment, observability and continuous improvement in production systems serving 500M+ users.

Champion an “always ship to production” team principle and partner with Platform, MLOps and Product Engineering teams to ensure models are deployed, monitored and measurably improving user outcomes.

Translate business and security priorities into a concrete applied-AI roadmap and sequence priorities across multiple domains.

Communicate trade-offs to senior leadership and report on delivery, model performance and impact.

Establish and evolve team practices for experiment tracking, model evaluation, reproducibility, code review and responsible AI in high-stakes cybersecurity environments.

About You:

M.Sc. or Ph.D. in Computer Science, Machine Learning, Mathematics or a related quantitative field, or equivalent practical experience with demonstrable deep-learning expertise.

5+ years of hands-on experience building and deploying machine-learning and deep-learning systems in production.

2-3+ years of experience leading or managing a small team of ML / Research Engineers, with responsibility for technical direction, delivery, hiring and people development across multiple applied AI areas.

Deep hands-on expertise across several applied-AI areas, such as modern transformer-based Small Language Models, reinforcement learning (PPO, DQN, Actor-Critic and variants), agentic / tool-using LLM systems, computer vision or classical ML.

Ability to select the right technique for each problem rather than defaulting to one approach.

Expert proficiency in Python and PyTorch. Experience with JAX or TensorFlow is a plus.

Experience training and fine-tuning models on multi-GPU clusters using frameworks such as DeepSpeed, FSDP or Hugging Face Accelerate.

Strong mathematical foundation in linear algebra, optimization, probability and statistics, with the ability to read, evaluate and implement approaches from recent research papers.

Proven track record of taking AI models all the way to production, including data pipelines, evaluation harnesses, quantization / compression, inference optimization, deployment using containers, Kubernetes and cloud, and post-deployment monitoring.

Experience with MLOps and experiment-management tooling such as Weights & Biases, MLflow or similar.

Experience with modern software-engineering practices including CI/CD, code review and observability.

Demonstrated ability to lead a small technical team, including setting direction, running effective 1:1s, giving concrete feedback, hiring strong engineers and turning ambiguous business problems into shippable AI systems.

Fluent English for effective communication within an international research and engineering organization.

Hands-on technical leadership style and willingness to stay close to the code, models and production systems.

Player-coach mindset, with the ability to combine technical problem-solving with people leadership and career development.

Strong bias for shipping and a focus on deploying and measuring models in production.

Excellent judgment when evaluating a portfolio of applied-AI opportunities, including knowing when to invest in a novel approach and when to choose the simplest solution that works.

Clear communication skills, with the ability to translate complex ML concepts into decisions and trade-offs for product, security and executive stakeholders.

Ability to thrive in a fast-paced, ambiguous, high-tech environment and manage complex cross-functional problems with calm and structure.

Nice to Have:

Publications at top-tier ML venues such as NeurIPS, ICML, ICLR, ACL or CVPR.

Applied-AI experience in cybersecurity, privacy or identity.

Experience with JAX or TensorFlow.

What's Next:

After you submit your application, you can expect the following steps in the recruitment process:

An interview with the Hiring Manager.

A technical interview with senior members of the AI Research Lab.

A cross-functional discussion with Product and stakeholder partners.

A final conversation with Leadership.