Senior Machine Learning Engineer / Researcher, Medal.tv
$250-300k
Offers Equity
Offers relocation package
5 days a week in office
Social platform for short-form gaming video clips
Be an early applicant
Social platform for short-form gaming video clips
21-100 employees
Be an early applicant
$250-300k
Offers Equity
Offers relocation package
5 days a week in office
21-100 employees
Company mission
To connect people by creating memories while they are physically apart!
Role
Who you are
- This is a highly technical role for someone who’s excited to work at the intersection of desktop software, native development, and AI integration, and who thrives in a fast-paced, collaborative environment
- We’re looking for someone who is interested in building the AI user experience for the future. We punch above our weight so we value extreme ownership, accountability, and proactiveness. The ideal candidate combines technical prowess with strong leadership skills. You might be a good fit if you have:
- A Master’s or PhD in Computer Science, Machine Learning, Statistics, Applied Math or equivalent experience
- Solid understanding of ML fundamentals: supervised & unsupervised learning, deep learning, model evaluation metrics, deployment, inference latency trade-offs
- Familiarity with knowledge representation and retrieval: building or leveraging knowledge graphs, embeddings, and memory systems to support contextual inference and long-term model learning
- 5+ years of hands-on experience building machine learning models in production environments – we ship features fast, so we need someone who can keep up
- Experience deploying models at scale in production (AWS/GCP/Azure)
- Experience with data engineering (ETL pipelines, feature stores, large datasets) and production-grade MLOps (CI/CD, monitoring, retraining workflows)
- Bonus / preferred:
- Published research or open-source contributions
- Experience with genAI (LLMs, diffusion models), computer vision, multimodal ML
- Knowledge of prompt engineering, RAG, embeddings
- Ownership mentality (no task is too small for you) – it’s all hands on deck here and we need everyone rowing the boat
- Deep understanding of system architecture and design patterns – we’re looking for someone who can hit the ground running day one
- Excellent communication skills and a collaborative mindset – we’re extremely lean so we’re looking for a team player who works well with others
- Type-A tendencies: maybe you’re extremely detail-oriented. Or maybe you take pride in shipping high-quality products. Whatever it is you do, you give it your all
- Must be based in or willing to relocate to NYC – although we’re flexible with days off & schedules, we have a 100% in-office culture during the week
- And even if you don’t fall neatly into any of these buckets, we’re looking for scrappy and motivated self-starters above all. Hustle is expected, grit is required
What the job involves
- As a Senior Machine Learning Engineer, you will drive the design, development, and deployment of advanced ML models and systems that power our core product features. You will collaborate closely with product, engineering, and data teams to translate research into production-ready services, while also advancing our longer-term R&D roadmap
- Design and implement knowledge graph or memory systems to enable contextual retrieval, reasoning, and persistent knowledge for the core Highlight application
- Research, prototype and deploy state-of-the-art machine learning text and OCR models (e.g., transformer architectures, computer vision, time series forecasting, reinforcement learning)
- Build pipelines for data ingestion, feature engineering, model training, evaluation, and deployment
- Work on productionizing models: monitoring, scalability, latency, retraining, A/B testing, and model lifecycle management
- Develop infrastructure and tooling to support ML experimentation and production (e.g., model serving, MLOps, observability)
- Collaborate with cross-functional partners (product managers, software engineers, UX researchers) to integrate ML features into product flows
- Keep abreast of latest ML/AI research, propose novel approaches, publish (internally or externally) when appropriate
- Participate in architecture discussions & technical direction for ML/AI at the company
Our take
Video games have become highly social experiences due to multiplayer gaming, eSports, and social media, spawning a huge market for audiences who enjoy watching gaming-related video content. Gamers commonly stream games and edit the footage to produce memes and engage with friends, but this requires many software tools and a separate platform on which to share experiences.
Medal.tv provides a social platform for gaming video clips, allowing users to record and edit footage, make gaming memes, and share content with friends within the same software. The platform collects in-game data to mitigate cheating and cyberbullying and integrates directly into any Windows or Android game.
Whilst the company will face great competition from platform giants such as Youtube, TikTok, and Twitch, Medal.tv shows the potential to stand out by being specifically designed for gaming content. Furthermore, the platform’s accessible video capturing and editing interface keeps the end-to-end social gaming experience within the same platform, making it more convenient and immersive for gamers than on other platforms.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
-21% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Jul 2024
$13m
LATE VC
Dec 2021
$45m
SERIES C
Total funding: $85.9m
Company HQ
Naarden, Netherlands
Leadership
Pim de Witte
(Co-Founder & CEO)Co-founder and CEO of Highlight. Experience as Founder, CEO & CTO of Soulsplit from 2009 to 2014. Software Engineer and Product Manager for Médecins Sans Frontières (MSF) from 2014 to 2017.
Joshua Lipson
(Co-Founder & Chief Architect)Co-founder and Chief Architect at Highlight. Full Stack Developer and CTO of Soulsplit from 2013 to 2015.
Iggy Harmsen
(Co-Founder & Product Design)Worked in CS & Marketing for Soulsplit from 2012 to 2015.
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