Reacher Platforms Inc
About Reacher:
We're the
#1 TikTok Shop partner
helping brands like Under Armour, Hanes, HeyDude, and Logitech scale their affiliate marketing. We've crossed
7 figures in ARR , and are rapidly scaling our team this year. Our vision is to become the
Hubspot for creator marketing , powering brands and creators to connect and grow across all commerce platforms (Youtube Shopping, Instagram Shopping, Shopify, Amazon).
We're building key infrastructure for the creator economy and implementing
AI with the world's largest brands and creators . Your work will
directly impact users on day 1 —our user base depends on our product daily.
Your work will directly impact users on day 1.
We have a highly responsive user base that depends on our product day in and day out.
What You'll Do
Own ML systems end-to-end : research, prototype, train, deploy, and iterate rapidly
Build multimodal ML systems
for video, text, images, and audio at scale
Design and deploy LLM-powered applications
using RAG and AI APIs
Develop content understanding and classification models
for text and visual data
Build search and discovery systems
using embeddings and semantic retrieval
Create audio analysis and processing pipelines
Build MLOps infrastructure —data pipelines, model serving, monitoring, and experiment tracking
Ideate ML/AI product features
with product and customers
Leverage modern AI tools
to accelerate development
Work directly with customers and translate vague requirements into shipped ML solutions
Ship fast and learn fast —high urgency environment
You're a Fit If
You have
4–8 years of ML engineering experience
building and deploying models in production
You have
strong Python and ML fundamentals
and write clean, maintainable production code
You've
built ML models end-to-end : data pipelines, training, serving, monitoring
You’re
experienced with modern ML frameworks
(PyTorch, TensorFlow, scikit‑learn)
You have
production experience with LLMs and AI APIs
(OpenAI, Anthropic, Hugging Face)
You can
build ML systems across multiple domains —NLP, computer vision, and audio
You’re
product‑minded
and identify where ML can solve user problems and improve business metrics
You’re
comfortable with MLOps tools
and cloud platforms (AWS or GCP)
You’re
resourceful and thrive in ambiguous environments
ML & AI‑Specific Skills We Value
Core ML : Supervised/unsupervised learning, feature engineering, model evaluation, A/B testing
Deep Learning : Neural networks, transformers, CNNs, training and optimization
NLP/LLMs : RAG systems, prompt engineering, vector databases, fine‑tuning, LangChain
Computer Vision : Image classification, object detection, OCR, visual content understanding, image embeddings
Audio/Speech : Audio classification, speech recognition (ASR), audio transcription
Search & Retrieval : Semantic search, embedding models, vector similarity, multimodal retrieval
MLOps : Model serving, monitoring, experiment tracking (MLflow, Weights & Biases), data pipelines
Cloud ML : AWS (SageMaker, Bedrock) or GCP (Vertex AI), model deployment, scalable inference
Bonus Points If
You've worked at an
early‑stage startup
or been a
first/early ML hire
You have experience building
multimodal search systems
or semantic retrieval at scale
You have experience with
video understanding —extracting features, generating embeddings, or analyzing visual content
You've implemented
feature stores
or advanced MLOps infrastructure
You have experience with
real‑time inference and low‑latency model serving
You have expertise in
adversarial robustness
and model safety testing
You've built
hybrid RAG + fine‑tuning systems
You have experience with
multimodal models
(vision + language, audio + text)
You're experienced with
model optimization
(quantization, distillation, pruning)
You have a track record of
shipping ML‑driven product features
that moved key metrics
You've shipped side projects or contributed to open‑source ML projects
Why Join Us
Post-revenue company solving
real problems for real customers
Be the ML/AI leader —define our ML strategy and infrastructure as we scale
High autonomy and visibility —no boring tickets
Your models reach users
within days, not months
Strong
engineering‑first culture
Shape both the product and the company
Work on diverse ML problems across video, language, and audio
Direct impact on product strategy —your ML ideas become features
When Applying: Feel free to send a short note about
something you've built
and
why this role excites you . GitHub/LinkedIn/resume is great, but we care more about
how you think and build !
#J-18808-Ljbffr
#1 TikTok Shop partner
helping brands like Under Armour, Hanes, HeyDude, and Logitech scale their affiliate marketing. We've crossed
7 figures in ARR , and are rapidly scaling our team this year. Our vision is to become the
Hubspot for creator marketing , powering brands and creators to connect and grow across all commerce platforms (Youtube Shopping, Instagram Shopping, Shopify, Amazon).
We're building key infrastructure for the creator economy and implementing
AI with the world's largest brands and creators . Your work will
directly impact users on day 1 —our user base depends on our product daily.
Your work will directly impact users on day 1.
We have a highly responsive user base that depends on our product day in and day out.
What You'll Do
Own ML systems end-to-end : research, prototype, train, deploy, and iterate rapidly
Build multimodal ML systems
for video, text, images, and audio at scale
Design and deploy LLM-powered applications
using RAG and AI APIs
Develop content understanding and classification models
for text and visual data
Build search and discovery systems
using embeddings and semantic retrieval
Create audio analysis and processing pipelines
Build MLOps infrastructure —data pipelines, model serving, monitoring, and experiment tracking
Ideate ML/AI product features
with product and customers
Leverage modern AI tools
to accelerate development
Work directly with customers and translate vague requirements into shipped ML solutions
Ship fast and learn fast —high urgency environment
You're a Fit If
You have
4–8 years of ML engineering experience
building and deploying models in production
You have
strong Python and ML fundamentals
and write clean, maintainable production code
You've
built ML models end-to-end : data pipelines, training, serving, monitoring
You’re
experienced with modern ML frameworks
(PyTorch, TensorFlow, scikit‑learn)
You have
production experience with LLMs and AI APIs
(OpenAI, Anthropic, Hugging Face)
You can
build ML systems across multiple domains —NLP, computer vision, and audio
You’re
product‑minded
and identify where ML can solve user problems and improve business metrics
You’re
comfortable with MLOps tools
and cloud platforms (AWS or GCP)
You’re
resourceful and thrive in ambiguous environments
ML & AI‑Specific Skills We Value
Core ML : Supervised/unsupervised learning, feature engineering, model evaluation, A/B testing
Deep Learning : Neural networks, transformers, CNNs, training and optimization
NLP/LLMs : RAG systems, prompt engineering, vector databases, fine‑tuning, LangChain
Computer Vision : Image classification, object detection, OCR, visual content understanding, image embeddings
Audio/Speech : Audio classification, speech recognition (ASR), audio transcription
Search & Retrieval : Semantic search, embedding models, vector similarity, multimodal retrieval
MLOps : Model serving, monitoring, experiment tracking (MLflow, Weights & Biases), data pipelines
Cloud ML : AWS (SageMaker, Bedrock) or GCP (Vertex AI), model deployment, scalable inference
Bonus Points If
You've worked at an
early‑stage startup
or been a
first/early ML hire
You have experience building
multimodal search systems
or semantic retrieval at scale
You have experience with
video understanding —extracting features, generating embeddings, or analyzing visual content
You've implemented
feature stores
or advanced MLOps infrastructure
You have experience with
real‑time inference and low‑latency model serving
You have expertise in
adversarial robustness
and model safety testing
You've built
hybrid RAG + fine‑tuning systems
You have experience with
multimodal models
(vision + language, audio + text)
You're experienced with
model optimization
(quantization, distillation, pruning)
You have a track record of
shipping ML‑driven product features
that moved key metrics
You've shipped side projects or contributed to open‑source ML projects
Why Join Us
Post-revenue company solving
real problems for real customers
Be the ML/AI leader —define our ML strategy and infrastructure as we scale
High autonomy and visibility —no boring tickets
Your models reach users
within days, not months
Strong
engineering‑first culture
Shape both the product and the company
Work on diverse ML problems across video, language, and audio
Direct impact on product strategy —your ML ideas become features
When Applying: Feel free to send a short note about
something you've built
and
why this role excites you . GitHub/LinkedIn/resume is great, but we care more about
how you think and build !
#J-18808-Ljbffr