Technical Leader, Data Engineering & ML Platform

Position Overview

We're seeking a hands-on technical leader with engineering velocity who owns end-to-end delivery of ML products from research to production—including edge deployment . You'll architect transformer models, optimize distributed training, build MLOps infrastructure, and leverage AI agents to automate workflows and accelerate productivity . Lead with an abundance mindset and "can-do" attitude , writing code, debugging Ray clusters, fine-tuning LLMs, and relentlessly removing friction.

Essential Responsibilities/Duties

Hands-On Technical Leadership & Velocity

• Roll up your sleeves : Build models, debug distributed training, optimize transformer inference (cloud & edge), review ML code
• Architect self-service ML platform on Databricks/MLflow that dramatically accelerates time-to-production
• Deploy AI agents for automation: code generation, pipeline optimization, testing, documentation, monitoring alerts
• Fine-tune and deploy transformer models using Huggingface ; optimize for latency, cost, and edge constraints
• Build fast iteration loops : automated training pipelines using Spark/Ray , one-click deployments, instant rollbacks
• Design edge inference solutions: model quantization, TFLite/ONNX conversion, on-device optimization
• Eliminate bottlenecks : use AI-assisted tooling to automate manual processes, generate boilerplate, accelerate development
• Establish CI/CD for ML: automated testing, validation, deployment enabling continuous releases
  
  

AI-Powered Productivity & Automation

• Leverage AI agents to multiply team productivity: automated code reviews, test generation, feature engineering suggestions
• Build AI-powered developer tools: intelligent debugging assistants, automated documentation, performance optimization
• Use LLMs for workflow automation: data quality checks, model evaluation reports, incident triage
• Create AI copilots for common tasks: pipeline creation, model deployment, configuration management
• Experiment with agentic workflows for complex automation: multi-step data processing, autonomous model tuning
  
  

End-to-End ML Product Delivery at Speed

• Own delivery from experimentation → production (cloud & edge) with aggressive focus on cycle time reduction
• Ship MVPs quickly; iterate based on production feedback
• Remove blockers relentlessly through hands-on problem solving
• Build MLOps infrastructure: automated retraining, A/B testing, monitoring, drift detection
• Measure and optimize velocity metrics: deployment frequency, lead time, automation coverage
  
  

Abundance Mindset & High-Velocity Culture

• Build force-multiplier platforms : enable data scientists to ship faster through automation and AI tooling
• Create reusable components, AI-powered templates, and patterns eliminating repetitive work
• Share knowledge; reduce onboarding time through AI-generated documentation and interactive guides
• Foster "ship and iterate" culture: bias toward action, fast feedback loops, AI-assisted development
• Celebrate speed AND quality; make doing the right thing the fast thing
  
  

Team & Strategy

• Scale high-velocity ML team shipping production impact continuously
• Drive platform strategy focused on AI-powered developer productivity and automation
• Partner cross-functionally to reduce dependencies and enable parallel workstreams
  
  

Minimum Qualifications

• Deep hands-on ML/data science experience; proven track record leading technical teams
• Expert in Python, PyTorch/TensorFlow, transformers, Huggingface
• Production experience with Databricks, MLflow, Spark, Ray
• Deep expertise in LLMs : fine-tuning (LoRA, PEFT), optimization, RAG, embeddings
• Edge inference experience: model compression, quantization, TFLite/ONNX, on-device deployment
• Track record accelerating velocity : built platforms, automation, AI-powered developer tools
• Experience deploying AI agents for workflow automation and productivity enhancement
• Strong MLOps and CI/CD experience; with reducing deployment friction
  
  

Travel: 10%

Tech Stack

Databricks

• MLflow
• Huggingface
• Spark
• Ray
• PyTorch
• TensorFlow Lite
• ONNX
• LangChain/LlamaIndex
• AI Agents
• Docker
• Kubernetes
• CI/CD
• AWS/GCP/Azure
  
  

Note: The Company reserves exclusive right in its sole discretion to modify, adjust, delete, add or otherwise change the above at any time.

SoundThinking provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, SoundThinking complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. SoundThinking maintains a drug-free workplace policy.

SoundThinking expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of SoundThinking’s employees to perform their job duties may result in discipline up to and including discharge. If you are an individual with a disability and require a reasonable accommodation to complete any part of the application process, or are limited in the ability or unable to access or use this online application process and need an alternative method for applying, you may contact SoundThinking at careers@soundthinking.com for assistance.

The above salary is based on a good faith estimate made at the time of publication and may be modified in the future. The pay offered to a candidate may vary within this range depending on factors such as education, experience, and geographic location.