ML Engineer

3 days ago

Hyderabad Telangana India, Telangana Fuzure Full time

Company Description

Fuzure Consultancy Services is a rapidly expanding Staffing and Software services organization headquartered in Hyderabad, India. Established in 2012, Fuzure has been driving innovation and excellence in the industry, earning the trust of numerous large and mid-sized organizations globally. We deliver exceptional solutions across diverse industries, specializing in Staffing & HR Solutions, Software Consulting, and R&D Center Management. Our dedicated team is committed to meeting client requirements and exceeding expectations.


Job Title:

ML Engineer – RL Environments

About the Role

We are looking for a highly autonomous Machine Learning Engineer who can design and implement SWE-Bench-style RL environments and generate a continuous stream of evaluation tasks for LLMs and agentic systems. This role is heavily engineering-focused and requires strong experience building custom environments, workflows, and code-based tasks.

You will work directly with a senior researcher but execute independently.


Responsibilities

● Build custom RL environments inspired by SWE-Bench, code-debugging tasks, unit-test-driven workflows, and agent evaluation tasks.

● Create large volumes of structured tasks, including:

  • code reasoning tasks
  • multi-step workflows
  • debugging challenges
  • reward-driven evaluation episodes
  • Define state/action/reward formats for each environment.
  • Implement task infrastructure in Python
  • Produce JSON schemas, templates, and reproducible task scripts.
  • Build testing harnesses to validate correctness of tasks.
  • Work closely with a researcher to align on quality, difficulty, and output structure.
  • Stay current with LLM evaluation and agentic frameworks.


Required Skills

● 4–5+ years of ML Engineering experience (title must reflect ML Engineer / RL Engineer / ML Research Engineer).

● Strong Python engineering background — building production-ready code and modular libraries.

● Experience with RL environment creation (Gym, Gymnasium, custom RL tasks).

● Experience with SWE-Bench, code evaluation, repo-based tasks, or similar systems is a major advantage.

● Strong understanding of reward shaping, episode design, and environment logic.

● Hands-on ML experience (PyTorch, TensorFlow, HF).

● Ability to independently generate tasks without supervision.

● Strong familiarity with LLMs and evaluation frameworks.


Nice to Have

● Prior work with LLM agent frameworks.

● Experience building debugging/patching tasks.

● Research engineering experience.


What success looks like

● You can independently produce new RL tasks daily.

● You write clean, reusable environment code.

● You understand how LLMs fail and design tasks to measure that.

● You need minimal oversight.

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