Software and Applied Machine Learning Engineer
AthleteDen | Product Engineering • Applied ML • Mobile • Cloud
About the Role
AthleteDen develops intelligent mobile software that combines media processing, on-device machine learning, data systems, and cloud services. We are looking for a hands-on engineer who can work across product development, applied ML, infrastructure, production operations, and customer needs.
This is a broad startup role with meaningful end-to-end ownership. You will help identify problems, shape solutions, implement and validate them, release them to customers, monitor their performance, and improve them through real-world feedback. The role is best suited to someone who enjoys moving between Swift, Python, data, models, cloud systems, and direct customer conversations.
Key Responsibilities
Software and iOS Product Development
- Own customer-facing features from product definition and technical design through implementation, testing, release, and ongoing maintenance.
- Develop and maintain a production iOS application using Swift, SwiftUI, UIKit, AVFoundation, Vision, Core ML, and related Apple frameworks.
- Build reliable camera, media capture, processing, playback, visualization, authentication, onboarding, profile, settings, and user-support experiences.
- Improve application architecture, modularity, accessibility, maintainability, and performance while supporting multiple device capabilities and iOS versions.
- Develop supporting Python services, internal tools, automation, and command-line workflows used by the mobile product and ML lifecycle.
Applied AI and Machine Learning
- Own the applied-ML lifecycle, including data collection, annotation, preprocessing, augmentation, training, evaluation, deployment, monitoring, and retraining.
- Develop and improve computer-vision, temporal-analysis, audio-analysis, and sensor-fusion models for mobile product experiences.
- Create reproducible training and validation pipelines using Python and frameworks such as PyTorch, TensorFlow, OpenCV, or equivalent tools.
- Perform structured error analysis across device types, environmental conditions, capture configurations, and representative real-world usage.
- Maintain clear dataset lineage, train-validation-test separation, experiment records, model versions, release notes, and reproducible evaluation fixtures.
- Translate model capabilities and limitations into product behavior that is understandable, useful, and safe for customers.
Mobile Model Optimization
- Convert trained models to Core ML and integrate them into Vision-based and custom on-device inference pipelines.
- Optimize accuracy, latency, memory use, application size, battery consumption, and thermal behavior for production mobile devices.
- Apply appropriate techniques such as quantization, reduced precision, frame sampling, batching, adaptive scheduling, and staged analysis.
- Benchmark on physical devices under realistic conditions and identify bottlenecks across preprocessing, inference, post-processing, persistence, and rendering.
- Design graceful behavior for memory pressure, interrupted work, long media inputs, unsupported capabilities, and degraded network conditions.
Data Handling and Persistence
- Design and maintain dependable data models for user content, processing sessions, model outputs, generated artifacts, and operational state without exposing product-specific implementation details.
- Manage local persistence using SQLite or equivalent technologies, including schema migrations, transactions, repositories, indexing, and data-integrity safeguards.
- Build offline-first synchronization, retry handling, conflict management, background processing, and recovery workflows.
- Create data ingestion, cleanup, validation, labeling, export, and audit tools that keep model-development inputs trustworthy and reproducible.
- Establish appropriate retention, deletion, backup, and access practices for customer data, diagnostic data, datasets, and generated files.
Cloud and Backend Operations
- Manage services across Google Cloud, Firebase, and AWS, selecting appropriate managed services for the product’s reliability, security, and scale requirements.
- Operate storage, serverless functions, background processing, authentication, databases, messaging, and containerized workloads.
- Build cloud-assisted ingestion, processing, dataset organization, annotation, and model-development workflows.
- Maintain development, testing, and production environments with secure configuration, service accounts, IAM policies, secret management, and least-privilege access.
- Monitor reliability, performance, usage, and cost; investigate failures across the mobile application, backend services, and ML pipelines.
Security, Privacy, and Reliability
- Apply secure programming practices throughout mobile, backend, cloud, data, and ML systems.
- Protect credentials, personal information, customer content, model assets, diagnostic packages, and sensitive configuration throughout their lifecycle.
- Prevent secrets and sensitive data from entering source control, application logs, analytics, error messages, or shared development artifacts.
- Design authentication, authorization, input validation, storage, network access, and account recovery with privacy and abuse prevention in mind.
- Follow Apple platform requirements, privacy guidance, and store policies while proactively identifying security, data-loss, and reliability risks.
Testing and Quality Engineering
- Write and maintain unit, integration, regression, performance, and end-to-end tests using XCTest and appropriate Python testing frameworks.
- Test model integration, numerical calculations, persistence boundaries, migrations, media workflows, memory constraints, logging, feedback submission, and recovery behavior.
- Validate features on simulators and physical devices across supported operating-system versions and hardware capabilities.
- Turn important customer-reported and field-discovered failures into reproducible fixtures and automated regression tests.
- Participate in code review, establish practical quality gates, and balance release speed with correctness, maintainability, and customer impact.
Release, Observability, and Operational Ownership
- Own the development-to-production lifecycle, including dependencies, build configuration, code signing, provisioning, TestFlight, App Store submission, and post-release maintenance.
- Manage application versions, build numbers, model versions, database migrations, release notes, backward compatibility, and phased rollout considerations.
- Build useful structured logging, diagnostics, crash reporting, performance monitoring, and support packages that make production issues easier to reproduce.
- Monitor production health, investigate device- and input-specific problems, coordinate fixes, validate releases, and communicate status clearly.
- Maintain technical documentation, architectural decisions, setup instructions, changelogs, runbooks, and troubleshooting guides.
Customer-Facing and Startup Responsibilities
- Communicate directly with customers, beta users, and other stakeholders to understand workflows, pain points, and expectations.
- Provide responsive technical support and explain complex product or engineering issues in clear, customer-friendly language.
- Triage feedback, diagnostic logs, crash reports, and field-test results; prioritize issues by urgency, customer impact, and business value.
- Turn customer feedback into actionable product improvements and close the loop after fixes or enhancements are released.
- Participate in demonstrations, onboarding, testing sessions, product discovery, and fast iteration in response to changing priorities.
- Take ownership beyond code completion, from identifying the underlying problem through verifying that the delivered experience works for the customer.
Required Qualifications
- Strong software engineering fundamentals and experience owning production systems or substantial customer-facing features.
- Proficiency in Swift and practical experience developing, shipping, and maintaining iOS applications with SwiftUI and UIKit.
- Experience with AVFoundation, Core ML, Vision, media processing, sensor data, or another performance-sensitive mobile domain.
- Applied machine-learning experience using Python and a framework such as PyTorch or TensorFlow.
- Experience preparing datasets, designing experiments, evaluating models, diagnosing failure cases, and deploying models into real products.
- Understanding of mobile model optimization and the tradeoffs among model quality, latency, memory, battery, thermals, and package size.
- Experience with databases, cloud-connected applications, APIs, authentication, asynchronous work, and offline data flows.
- Working knowledge of Google Cloud, Firebase, AWS, or comparable cloud platforms.
- Strong testing, debugging, logging, documentation, Git, code-review, and workflow-management practices.
- Experience with iOS build, signing, beta distribution, App Store publishing, production monitoring, and application maintenance.
- Knowledge of secure coding, privacy-conscious data handling, credential management, and least-privilege access.
- Ability to communicate effectively with both technical collaborators and customers in a fast-paced, changing environment.
Preferred Qualifications
- Experience with Core ML Tools, OpenCV, Label Studio, FFmpeg, one-stage convolutional object-detection tooling, or comparable model and media workflows.
- Knowledge of temporal modeling, audio signal processing, depth sensing, sensor fusion, or physics-informed computation.
- Experience with SQLite or GRDB, transactional persistence, schema migrations, and offline-first synchronization.
- Hands-on experience with Firebase services, serverless infrastructure, containers, messaging, and cloud storage.
- Familiarity with observability, crash reporting, performance profiling, CI/CD, and customer diagnostic tooling.
- Previous experience as an early engineer, broad technical owner, or customer-facing developer in a startup.
What Success Looks Like
- Reliably deliver useful customer-facing software from concept through production release and maintenance.
- Improve model and application quality while reducing on-device processing time and resource use.
- Establish trustworthy, reproducible data, experiment, model-versioning, and validation practices.
- Reduce production issues through stronger testing, diagnostics, monitoring, documentation, and operational follow-through.
- Respond quickly to customer feedback and convert recurring problems into durable product and engineering improvements.
- Help build an engineering foundation that can scale with product usage, customer expectations, datasets, and future capabilities.
How We Work
We value practical ownership, sound engineering judgment, curiosity, direct customer empathy, and the ability to learn quickly. You should be comfortable working with incomplete requirements, shifting priorities, and short feedback cycles while maintaining quality, security, and reliability. Equivalent practical experience is valued; a specific degree is not required.
