Tech Lead Manager, AI

About the Role

This is a hybrid IC/management role where you'll spend approximately 80% of your time on hands-on technical work and 20% on people management and team leadership. You'll own and drive the AI team building the intelligence layer at the heart of the platform—designing, building, and shipping the models, agents, and ML systems that power its predictive and prescriptive capabilities.

What You'll Do

Technical Leadership & Execution (~80%)

• Own the architecture and delivery of AI features end-to-end—from data ingestion and feature engineering to model training, serving, evaluation, and the product surfaces they power.
• Design and build systems behind forecasting, recommendation, and agentic planning capabilities, including LLM-based pipelines, classical ML models, and hybrid approaches trained on per-customer historical data.
• Drive engineering excellence: lead architecture discussions for model training and inference infrastructure, set standards for offline/online evaluation, experimentation, and responsible AI.
• Make pragmatic technical decisions that balance model quality, latency, cost, and long-term system health.
• Leverage modern infrastructure including PostgreSQL, Redis, Kubernetes, vector stores, and streaming technologies.
• Champion AI quality—accuracy, calibration, robustness, latency, and guardrails that make AI outputs trustworthy in an enterprise context.
• Implement best practices in evaluation, observability, drift detection, and A/B testing.

People Management & Team Leadership (~20%)

• Manage, mentor, and grow a team of ML engineers and applied AI engineers, owning their career growth, performance reviews, and professional development.
• Conduct regular 1:1s, provide timely and constructive feedback, and create individual development plans.
• Foster a culture of psychological safety, trust, accountability, and continuous improvement.
• Own team planning: scope AI work with Product and Design, participate in sprint planning and Agile ceremonies, and remove blockers.
• Drive hiring for the team—defining roles, conducting interviews, and making hiring decisions.

Cross-Functional Collaboration

• Partner with Product Managers and Designers to translate product vision into well-defined AI problem statements and technical plans.
• Communicate progress, model performance, risks, and trade-offs clearly to engineering leadership and non-technical stakeholders.
• Collaborate across Engineering, Data, and Platform teams to drive alignment on shared data, features, evaluation infrastructure, and serving systems.

What We're Looking For

• B.S. or M.S. in Computer Science, Machine Learning, or a related field, or equivalent experience.
• 7+ years of hands-on software engineering experience with a strong applied ML background—shipping production ML systems, not just prototypes or research.
• 2+ years of engineering management or tech lead experience, including direct reports, mentorship, and team-level delivery ownership.
• Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, or equivalent), plus comfort in at least one backend language for productionizing services.
• Deep understanding of the applied ML lifecycle: problem framing, data pipelines, feature engineering, training, evaluation, deployment, and monitoring.
• Hands-on experience with LLMs and modern AI tooling—prompt design, retrieval-augmented generation, fine-tuning, agentic workflows, and evaluation of non-deterministic systems.
• Experience with RESTful APIs, relational databases (PostgreSQL), vector databases, and cloud-native architecture (Kubernetes, containerization, microservices).
• Excellent communication skills—able to translate complex ML concepts for both engineers and non-technical stakeholders.
• Early-stage startup experience (Seed to Series C) preferred.

Nice to Have

• Experience building agentic systems, tool-using LLM pipelines, or multi-step reasoning workflows in production.
• Familiarity with time-series forecasting, recommendation systems, or workforce/operations modeling.
• Background in MLOps tooling (MLflow, Weights & Biases, Ray, Kubeflow) or large-scale data pipeline orchestration (Airflow, Dagster, Prefect).
• Experience with real-time analytics and streaming infrastructure such as Redis, Kafka, or Apache Pinot.
• Knowledge of evaluation frameworks for LLM-based systems and experience designing offline/online eval harnesses.

Why This Role

• High impact: deploy technology that changes how enterprises plan, manage, and scale their workforce.
• Deep technical ownership: work directly in code and ship meaningful solutions to real customers.
• Cross-functional exposure: operate at the intersection of Engineering, Product, and Design.
• Competitive compensation, meaningful equity, world-class benefits, and a flexible remote-first culture.

Pay: $200,000.00 - $235,000.00 per year

Benefits:

• 401(k)
• 401(k) matching
• Dental insurance
• Employee assistance program
• Employee discount
• Flexible schedule
• Flexible spending account
• Health insurance
• Health savings account
• Life insurance
• Paid time off
• Parental leave
• Professional development assistance
• Referral program
• Retirement plan
• Tuition reimbursement
• Vision insurance

Application Question(s):

• Tell us about your early-stage startup experience (Seed to Series C):

Education:

• Bachelor's (Preferred)

Experience:

• Engineering management/tech lead direct reports, mentorship: 2 years (Preferred)
• Python, modern ML frameworks (PyTorch, TensorFlow): 3 years (Preferred)
• Hands-on software engineering: 7 years (Preferred)

Location:

• San Francisco, CA 94102 (Preferred)

Ability to Commute:

• San Francisco, CA 94102 (Preferred)

Work Location: Hybrid remote in San Francisco, CA 94102