Senior Machine Learning Engineer - Bangalore

Below is a ready‑to‑send cover‑letter template (with placeholders you can fill in with your own details) that highlights the experience and skills Warner Bros. Discovery is looking for in a Senior Machine Learning Engineer.

Feel free to copy‑paste it into your email or application portal, then replace the bracketed sections with your personal information, specific projects, and metrics. I’ve also added a short “quick‑facts” bullet list you can attach to your résumé to make the match crystal‑clear at a glance.

📄 Sample Cover Letter – Senior Machine Learning Engineer (Bangalore)

[Your Name]
[Your Street Address]
Bangalore, Karnataka, India [PIN]
[Phone] • [Email] • [LinkedIn] • [GitHub/Portfolio]

[Date]

Hiring Committee – Warner Bros. Discovery
[Company Address – if known]
Bangalore, India

Dear Hiring Committee,

I am excited to apply for the **Senior Machine Learning Engineer** position on the WBD Content & Advertising Platform team in Bangalore. With **7 years of end‑to‑end ML system design**, a proven track record of shipping production‑grade deep‑learning pipelines at scale, and a passion for turning media‑rich data into actionable insights, I am confident I can help Warner Bros. Discovery unlock the next generation of content‑driven experiences.

### Why I’m a strong fit

| Requirement | My experience (with impact) |
|-------------|-----------------------------|
| **5‑8 years designing & building ML algorithms** | Built a **multilingual captioning service** (English, Hindi, Tamil) that reduced manual transcription cost by **68 %** and improved WER from 12 % to 5 % for 30 M+ daily video minutes. |
| **Proficiency in Java, Golang, Python, Scala** | Core contributor to a **Java‑based feature‑store** (Apache Flink + Scala) and authored **Python‑PyTorch** training scripts for vision‑language models. |
| **Distributed systems & cloud (AWS)** | Designed a **Kubernetes‑native ML platform** on AWS EKS that auto‑scales training jobs from 1 to 256 GPUs, cutting time‑to‑model from 48 h to 3 h. |
| **End‑to‑end ML workflow (data, training, serving, monitoring)** | Implemented a **CI/CD pipeline** (GitHub Actions + Argo) for model versioning, A/B testing, and drift detection; achieved **99.9 % SLA** for real‑time ad‑targeting inference. |
| **Deep Learning, NLP, LLMs, CV, RL, Causal Inference** | • Developed a **BERT‑based metadata extractor** that lifted content‑tag recall by 22 %.<br>• Trained a **Vision Transformer** for automated thumbnail selection, increasing click‑through‑rate by 4.3 % in production.<br>• Ran **causal uplift experiments** on ad‑conditioning pipelines, delivering a 6 % lift in conversion. |
| **Experimentation & A/B testing** | Built a **feature‑flag framework** that enabled 150+ concurrent experiments; published weekly impact dashboards for product owners. |
| **Batch & streaming data processing** | Leveraged **Spark Structured Streaming** for real‑time feature generation and **AWS Glue** for nightly batch enrichment of 5 TB of raw media logs. |
| **PhD/Masters in CS or related** | M.S. in Computer Science, **University of Bangalore**, specialization in Machine Learning & Distributed Systems (CGPA 3.9/4.0). |

### What I’ll bring to WBD

* **Strategic vision** – I will partner with product, editorial, and ad‑ops teams to identify high‑impact ML opportunities across the media supply chain (e.g., automated content tagging, dynamic ad‑conditioning, and personalized recommendation).  
* **Hands‑on leadership** – I have mentored 12 engineers, instituted code‑review standards, and introduced **ML‑Ops best practices** (model registries, automated canary rollouts, observability).  
* **Scalable infrastructure** – My experience building cloud‑native pipelines on AWS (EKS, SageMaker, S3, EventBridge) aligns directly with WBD’s digital strategy and governance requirements.  
* **Culture of inclusion** – I champion diverse hiring panels and run monthly “ML‑for‑All” brown‑bag sessions to democratize AI knowledge across non‑technical teams.

I am thrilled by Warner Bros. Discovery’s mission to “bring the stuff dreams are made of” to audiences worldwide, and I am eager to contribute my expertise to the next wave of AI‑powered storytelling and advertising experiences.

Thank you for considering my application. I look forward to the opportunity to discuss how my background, skills, and passion can help WBD achieve its ambitious goals.

**Sincerely,**  
[Your Name]

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### Quick‑Facts Bullet List (to paste into your résumé)

- **7 y** full‑stack ML engineering (design → production) for media & ad tech.  
- **Python, Java, Golang, Scala** – 10 k+ lines of production code.  
- **AWS‑native**: EKS, SageMaker, S3, Lambda, CloudWatch; **Kubernetes** & **Docker** expertise.  
- **Deep Learning**: PyTorch, TensorFlow, HuggingFace Transformers; **CV & NLP** pipelines at >10 M req/day.  
- **ML‑Ops**: CI/CD (GitHub Actions, Argo), model registry (MLflow), monitoring (Prometheus, Grafana).  
- **A/B testing & causal inference**: 150+ experiments, 6 % lift in ad conversion.  
- **M.S. Computer Science**, University of Bangalore – thesis on “Scalable Distributed Feature Stores”.

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#### How to use this

1. **Replace** every bracketed placeholder (`[Your Name]`, `[Phone]`, etc.) with your actual details.  
2. **Quantify** any achievements you have that differ from the examples above (e.g., cost savings, latency improvements, user‑impact metrics).  
3. **Tailor** one or two sentences to mention a specific WBD product or initiative you admire (e.g., “I’m a long‑time fan of the HBO Max recommendation engine and would love to help evolve its next‑gen personalization stack”).  
4. **Attach** the Quick‑Facts list as a separate “Highlights” section in your résumé or as a one‑page addendum.  

Good luck with your application! 🎬🚀 If you’d like help polishing any specific project description, preparing for technical interview questions, or building a portfolio demo for the role, just let me know—I’m happy to dive deeper.