DS / ML Engineer
epifi· Data Science
About this role
Tetriz is an AI Engineering Intelligence platform that helps engineering organizations become AI-native, faster. We measure how AI coding tools like Cursor, Claude Code, and GitHub Copilot are actually used, improve engineer effectiveness through prompt and workflow coaching, and help engineering leaders demonstrate AI ROI with board-ready, defensible insights.
A builder mindset is the core of this role and where you'll spend most of your time. But we're a small team building a whole product, not a research lab. The best person here treats ML systems as their primary craft while staying willing to do whatever the product needs — thinking through the product itself, shipping backend or frontend code, untangling data pipelines. We're looking for someone energized by the breadth, not someone who wants to stay in their lane.
What you'll work onEvaluation systems for AI featuresHelp build the eval backbone our AI features ship against — failure taxonomies, LLM-as-judge rubrics, golden datasets, calibration against human judgment.Learn what it takes to keep automated scores honest as models and prompts change. A feature with no eval has no quality floor.
Model routing & inference economicsGet hands-on with how we route work across models — balancing cost, quality, and latency per task.Help run the experiments that justify those choices and catch regressions.
Scoring, measurement & signal qualityWork on turning noisy, real-world signals into scores you can actually trust — grounded in real statistical rigor, not vibes.Help move heuristic-driven approaches toward calibrated, monitored systems.
MLOps & productionGet exposure to the full lifecycle — feature pipelines, model versioning, rollout, monitoring for drift and silent quality decay.Work alongside engineering to see how models get served reliably at low latency.
What we're looking forMust have
A builder mindset is the core of this role and where you'll spend most of your time. But we're a small team building a whole product, not a research lab. The best person here treats ML systems as their primary craft while staying willing to do whatever the product needs — thinking through the product itself, shipping backend or frontend code, untangling data pipelines. We're looking for someone energized by the breadth, not someone who wants to stay in their lane.
What you'll work onEvaluation systems for AI featuresHelp build the eval backbone our AI features ship against — failure taxonomies, LLM-as-judge rubrics, golden datasets, calibration against human judgment.Learn what it takes to keep automated scores honest as models and prompts change. A feature with no eval has no quality floor.
Model routing & inference economicsGet hands-on with how we route work across models — balancing cost, quality, and latency per task.Help run the experiments that justify those choices and catch regressions.
Scoring, measurement & signal qualityWork on turning noisy, real-world signals into scores you can actually trust — grounded in real statistical rigor, not vibes.Help move heuristic-driven approaches toward calibrated, monitored systems.
MLOps & productionGet exposure to the full lifecycle — feature pipelines, model versioning, rollout, monitoring for drift and silent quality decay.Work alongside engineering to see how models get served reliably at low latency.
What we're looking forMust have
- 1.5–2 years of hands-on experience in Data Science, Machine Learning, Software Engineering, or a related role.
- Experience building and shipping DS/ML systems through professional work, personal projects, research, or open-source contributions — where you've built and run something end to end, not just notebooks.
- Comfort with Python and working SQL knowledge.
- Basic grounding in applied statistics — you can explain what a metric means and when it might be misleading.
- A builder's instinct — genuinely curious about product decisions, backend, or frontend, not just the modeling layer.
- Some exposure to LLMs — prompting, using APIs, or experimenting with model behavior.
Nice to have
- Any exposure to evaluation or observability tooling for LLM features.
- Experience with information retrieval, entity-matching, or record-linkage.
- Interest in developer-productivity, code analytics, or DevEx data.
We aspire to create an inclusive culture of diverse people not just because it's the right thing to do but because heterogeneity inspires us and is more fun! We employ people solely on merit and do not discriminate against any employee or applicant because of race, creed, color, religion, gender, sexual orientation, gender identity/expression
Frequently Asked Questions
Is the salary disclosed for the DS / ML Engineer position at epifi?
The salary for this DS / ML Engineer role at epifi is not publicly listed. Click "Apply Now" to learn more about the compensation package on their official careers page.
Where is the DS / ML Engineer position at epifi located?
This DS / ML Engineer role at epifi is based in Bangalore. The position is listed as on-site or hybrid. Check the full job description or apply directly to confirm the work arrangement.
Is the DS / ML Engineer role at epifi full-time or part-time?
This is listed as a Full time position. It is posted as a DS / ML Engineer role in the Data Science department at epifi.
Which team or department does the DS / ML Engineer at epifi belong to?
This DS / ML Engineer position is part of the Data Science department at epifi. See the full job description for more information about the team structure and responsibilities.
How do I apply for the DS / ML Engineer position at epifi?
Click the "Apply Now" button on this page. You will be redirected to epifi's official application portal hosted on lever where you can submit your application directly.
When was the DS / ML Engineer job at epifi posted?
This DS / ML Engineer position at epifi was posted on Jul 15, 2026. Apply as soon as possible — early applications are often reviewed first.
DS / ML Engineer
epifi
You'll be redirected to epifi's official application page on Lever.