Skip to content
CV Rocket
Get started
How it worksThe enginePricingBlogContactSign in

Open nowPosted 154 days ago

Applied ML Engineer

RemoteStar90 open roles

Where
Gurugram, India
Get the CV for this job

From $25 per CV, paid once. No subscription.

Your applicationOpen nowApplied ML EngineerRemoteStar · Gurugram, India

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on RemoteStar's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.6%14 days
  5. 35.6%30 days
  6. 45.1%60 days
This job: posted 154 days ago

The posting

Role: Applied ML Engineer

Location: Gurgaon (on-site) 5 days WFO

Employment type: Full-time

Company Overview:

Our client is revolutionising the gaming landscape as India’s first G-Commerce startup, bridging the gap between virtual achievements and real-world value. We empower gamers by transforming their in-game currencies and XP into tangible rewards, from exclusive brand discounts to physical goods, lowering cart values and making gaming more rewarding than ever.

Through strategic partnerships with game developers and top-tier brands, we create seamless white-labeled reward ecosystems, integrated directly into games and gaming platforms. Our mission? To redefine engagement by turning every play session into an opportunity for players to earn, redeem, and experience more.

Role Overview:

We are looking for an Applied ML Engineer with strong experience in recommender systems to build the brain of PlaySuper's in-game commerce store — a recommendation engine that decides which products, coupons, and rewards to surface to which player, at which moment.

This role is ideal for someone who has worked on:

- Collaborative filtering and embedding-based retrieval in production

- Recommendation systems for marketplaces, deals, or content feeds

- Cold-start and sparse-data problems

- Bridging offline model development to online serving

Rigorous evaluation and a bias for shipping are non-negotiable.

What You Will Do:

- Own the collaborative filtering model (starting with Gorse, potentially moving to a custom stack)

- Build product embeddings (product2vec + Faiss / ANN) for the PlaySuper catalogue

- Evolve cohort assignment from rules-based to ML-driven

- Build the offline evaluation framework — precision@k, NDCG, conversion-rate, diversity, coverage

- Bridge offline models to online serving (model serving infrastructure, weekly refresh pipeline)

- Calibrate ranking weights against business outcomes (CTR, GMV, margin, repeat redemption)

- Partner closely with the Data Engineer (event pipeline + feature store) and Backend Engineer (ranking API, Redis serving layer)

- Translate sparse, noisy in-game event data into reliable signal

- Act as the internal owner of recommendation quality — always pushing on whether the model is actually lifting outcomes vs. just looking good on offline metrics

What We Are Looking For:

- 3–5 years of ML engineering experience, with recommender systems specifically

- Strong Python: PyTorch / JAX, scikit-learn, NumPy

- Hands-on with collaborative filtering — sparse matrices, cold start, production evaluation

- Embedding-based retrieval experience (Faiss, ScaNN, or equivalent)

- Proper reco evaluation chops — beyond accuracy: diversity, coverage, business-outcome metrics

- Comfort with sparse and noisy data

- Experience taking models from offline notebooks to online serving in production

- Clear communication and structured problem-solving

Strong Plus (Nice to Have)

- Experience building voucher, coupon, or deal recommendation systems

- Gaming, mobile, or consumer-engagement product experience

- Familiarity with Gorse or LightFM

- Experience with contextual bandits or online learning

- Feature store patterns

- Startup experience or ownership in fast-moving environments

From $25, paid onceGet the CV for this job

What happens when you press

One press. We do the rest.

  1. A CV for this posting

    Written against RemoteStar's own wording, from every piece of relevant proof in your profile.

  2. 25 readers review it

    Recruiter, hiring manager, skeptic and more read every draft, round after round. You get the best round.

    The review screen in CV Rocket: how each CV was read, round by round.

  3. We apply on RemoteStar's form

    Our application engine gets through the hardest forms there are. Where a question needs you, AI suggests the best answer. Don't want us applying from our IP addresses? Use our Chrome extension: we apply straight from your own browser.

    An application in CV Rocket: every answer filled in on the employer's form.

  4. Every reply, sorted

    RemoteStar's answer lands in your private mailbox, and we classify it on arrival: interview, question, rejection.

    The CV Rocket inbox: each employer reply classified as an interview, an action or a rejection.

  5. Reply with AI

    AI helps you write the email, checks it and sends it. We show you whether the recruiter read it.

  6. The interview in your calendar

    Full integration with your calendar. The invitation goes straight in.

    An interview invitation in the CV Rocket inbox, added to the candidate's calendar.
Get the CV for this job

From $25 per CV, paid once. No subscription.

Why it works

3×

more interviews than doing it yourself with ChatGPT.

ChatGPT writes a CV and never learns what happened to it. We see every reply. For each CV we know:

  • How it was written, and how the review scored it
  • When we applied, and how long after the posting went up
  • Which posting, which company, which city
  • Who got the interview, and who heard nothing

That is how we know which CVs get called.

Get the CV for this job

From $25 per CV, paid once. No subscription.

The numbers game

More applications. More interviews.

Every application goes out with its own CV, written for that posting and paid once. Send enough of them and the law of large numbers finds you the job.

Get the CV for this job
By hand5–10
With CV Rocket100
applications a day

Nearby

Live postings like this one

Same employer first, then the same role elsewhere.

Before you press

Straight answers

Get the CV for this job

From $25 per CV, paid once. No subscription.

What if my background isn't good enough?

We make the most of the background you have. The CV uses every piece of relevant proof your profile holds, and one of the 25 readers reads your whole profile and flags what the CV left out.

Do you really apply for me?

Yes, on the employer's own form, the hardest ones included. Where a question needs you, you answer it right there and AI suggests the best answer. Don't want us applying from our IP addresses? Use our Chrome extension: we apply straight from your own browser.

Is it a subscription?

No. You pay once per CV, from $25. Every application goes out with its own CV, written for that posting.

One job. One CV.
Paid once.

Pick the posting you want. We write for it, apply for you and catch the reply.

Get the CV for this job

From $25 per CV, paid once. No subscription.