Technical Lead Manager

100% remote. Must reside in Bay Area.

Must have prior Start Up experience.

About the Role

We're looking for a Tech Lead Manager (TLM) to own and drive the AI team

building the intelligence layer at the heart of our Client's platform. This is a hybrid

IC/management role where you'll spend approximately 70% of your time on

hands-on technical work and 30% on people management and team leadership.

On the technical side, you'll design, build, and ship the models, agents, and ML

systems that power Our Client's predictive and prescriptive capabilities—from

forecasting workforce demand and flagging burnout risk to orchestrating LLM-

driven planning workflows trained on each customer's historical data. You'll write

production code, drive architectural decisions across model training, serving, and

Tech Lead Manager, AI 1

evaluation, and set the bar for applied ML quality. On the management side, you'll

build, mentor, and grow a high-performing team of ML and AI engineers, owning

their career development, performance, and day-to-day delivery.

The ideal candidate thrives at the intersection of applied ML depth and people

leadership—comfortable context-switching between shipping models and

coaching engineers. You'll set the technical direction for your team, partner

closely with Product, Design, and Data, and ensure your squad delivers AI

capabilities that are reliable, measurable, and shipped at a pace that matches

our Clients's growth trajectory.

What You'll Do

Technical Leadership & Execution (~70%)

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 the systems behind our Client's 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, and participate in the full ML lifecycle

from problem framing through deployment, monitoring, and on-call.

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 to power our Client's real-time AI

workflows.

Engage in hands-on coding, model development, code reviews, and

performance optimizations, setting the standard for applied ML excellence

across the team.

Tech Lead Manager, AI 2

Champion AI quality—accuracy, calibration, robustness, latency, and the

guardrails that make AI outputs trustworthy in an enterprise context.

Implement best practices in evaluation, observability, drift detection, and A/B

testing to ensure reliability and measurable customer impact.

People Management & Team Leadership (~30%)

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 for each report.

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 to build a world-class AI engineering team.

Maintain team health by monitoring workload, preventing burnout, and

ensuring sustainable delivery.

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.

Tech Lead Manager, AI 3

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

(Python, Node.js, or Ruby) 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.

Proven experience designing, building, and operating scalable ML systems in

data-heavy environments.

Solid grasp of software engineering best practices: testing, code review,

CI/CD, design documentation, reproducibility.

Experience with RESTful APIs, relational databases (PostgreSQL), vector

databases, and cloud-native architecture (Kubernetes, containerization,

microservices).

A systems-level thinker who balances model quality with pragmatic, business-

aware decision-making around cost, latency, and time-to-ship.

Excellent communication skills—you can translate complex ML concepts for

both engineers and non-technical stakeholders, and set realistic expectations

about what AI can and can't do.

Demonstrated ability to balance technical execution with people leadership—

comfortable context-switching between shipping models and coaching

engineers.

Early-stage startup experience (Seed to Series C) preferred—comfortable

wearing multiple hats and building in fast-moving environments.

Tech Lead Manager, AI 4

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.

Experience building and scaling AI teams in a high-growth startup

environment.

Knowledge of evaluation frameworks for LLM-based systems and experience

designing offline/online eval harnesses.

Why Join Our Client?

High Impact: Deploy technology that changes how enterprises plan, manage,

and scale their workforce.

Deep Technical Ownership: Work directly in code—not just configure

systems—and ship meaningful solutions to real customers.

Cross-Functional Exposure: Operate at the intersection of Engineering,

Product, and Design.

Growth & Learning: Build expertise in enterprise-scale systems, data

reliability, and AI-driven automation.

Benefits: Competitive compensation, meaningful equity, world-class

medical/dental/vision coverage, and a flexible remote-first culture with team

events, offsites, and happy hours.

If you're passionate about building AI systems at scale, love developing people as

much as models, and want to be part of a company growing at rocket speed, we'd

love to hear from you.